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Learn more: PMC Disclaimer | PMC Copyright Notice Br J Radiol . 2026 Jan 31;99(1180):802–811. doi: 10.1093/bjr/tqag024 Search in PMC Search in PubMed View in NLM Catalog Add to search Fronto-caudate and callosal microstructural alterations: unveiling multimodal MRI biomarkers in early Parkinson’s disease Ángela Bernabéu-Sanz Ángela Bernabéu-Sanz , PhD 1 Magnetic Resonance Department, Inscanner SL, Calle San Pedro Poveda 10, Alicante, CP 03010, Spain 2 Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain Find articles by Ángela Bernabéu-Sanz 1, 2, ✉ , Sandra Morales Sandra Morales , PhD 3 Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain Find articles by Sandra Morales 3, ✉ , Valery Naranjo Valery Naranjo , PhD 4 Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain Find articles by Valery Naranjo 4 , Eduardo Fernández Eduardo Fernández , MD, PhD 5 Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain 6 CIBER-BBN, Avenida Monforte de Lemos 3-5, Pabellón 11, Planta 0, Madrid, CP 28029 Find articles by Eduardo Fernández 5, 6 Author information Article notes Copyright and License information 1 Magnetic Resonance Department, Inscanner SL, Calle San Pedro Poveda 10, Alicante, CP 03010, Spain 2 Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain 3 Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain 4 Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain 5 Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain 6 CIBER-BBN, Avenida Monforte de Lemos 3-5, Pabellón 11, Planta 0, Madrid, CP 28029 ✉ Corresponding authors: Ángela Bernabéu-Sanz, PhD, Magnetic Resonance Department, Inscanner SL, Calle San Pedro Poveda10, CP 03010, Alicante, Spain ( [email protected] ); Instituto de Bioingienería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202, Elche, Spain ( [email protected] ); and Sandra Morales, PhD, Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Camino de Vera s/n, CP 46022, Valencia, Spain ( [email protected] ). Received 2025 Aug 7; Revised 2025 Dec 16; Accepted 2026 Jan 13; Collection date 2026 Apr. © The Author(s) 2026. Published by Oxford University Press on behalf of the British Institute of Radiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [email protected]. PMC Copyright notice PMCID: PMC13070641 PMID: 41619801 Abstract Objectives This study investigated grey and white matter alterations and their association with motor and cognitive symptoms in early-stage Parkinson’s disease (PD). Methods Thirty-one early-stage PD patients and 30 matched healthy controls underwent multimodal MRI (VBM, DTI) and comprehensive clinical/neuropsychological assessments. We assessed grey matter atrophy, white matter microstructure, and caudate-cortical connectivity. Results Parkinson’s disease (PD) patients showed selective deficits in memory (FCSRT total recall, P -FDR = .014) and processing speed (SDMT, P -FDR = .025). Voxel-based morphometry (VBM) revealed bilateral caudate atrophy (left, P -FDR = .024; right, P -FDR = .026). Diffusion tensor imaging (DTI) demonstrated widespread microstructural alterations in corpus callosum and major association tracts. Disease duration negatively correlated with corpus callosum streamline counts (superior parietal P-FDR = .02; posterior parietal P-FDR = .004). UPDRS negatively correlated with fractional anisotropy (FA) in occipital ( P-FDR = .002) and temporal ( P-FDR = .0017) corpus callosum segments. Reduced caudate-cortical streamline density in frontal regions correlated with UPDRS/FCSRT scores; caudate-cingulum streamlines correlated with Mini-Mental State Examination (MMSE) attention/calculation. Conclusions Our findings suggest early functionally relevant degeneration of fronto-caudate and interhemispheric pathways in PD. These structural changes correlate with specific cognitive and motor impairments, and are candidate imaging biomarkers for early PD progression and/or cognitive vulnerability. Advances in Knowledge This is the first tractography study to evaluate connectivity between the caudate nuclei and different frontal lobe regions, unveiling specific white matter alterations in early PD. Our findings suggest that caudate atrophy, though not directly correlated with clinical variables, may underlie or result from impaired caudate-cortical connectivity, potentially accounting for some of the multifaceted PD symptoms. Keywords: Parkinson’s disease, brain, diffusion tensor imaging, caudate connectivity Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuron loss in the substantia nigra pars compacta , driven by α-synuclein accumulation. While defined by its motor symptoms, PD pathology extends beyond the nigrostriatal system, affecting multiple brain regions and involving both dopaminergic and non-dopaminergic neurons. 1 Parkinson’s disease is increasingly recognized as a multisystem disorder, in which cognitive and neuropsychiatric impairments can appear early in the disease course. 2 These deficits, affecting memory, attention, and executive function, likely result from a complex interplay of α-synuclein pathology, neurotransmitter imbalances, and neuroinflammation. 2 , 3 Despite these early clinical manifestations, conventional Magnetic Resonance Imaging (MRI) often yields normal or non-specific findings, limiting its utility for early diagnosis and disease monitoring. 4 However, advanced neuroimaging is increasingly uncovering subtle structural and microstructural brain changes in PD. Voxel-based morphometry (VBM) studies consistently report cortical thinning in several brain regions, 5 , 6 but findings regarding subcortical structures, particularly the basal ganglia, remain inconsistent, with reports of caudate atrophy only in advanced stages, while others detecting it early. 7 , 8 Similarly, diffusion tensor imaging (DTI) studies show variable results, with some detecting no early microstructural abnormalities, 7 while others revealing alterations in brainstem and subcortical pathways. 9 These discrepancies underscore a critical gap in our understanding: whether early caudate nucleus atrophy is accompanied by widespread white matter (WM) damage and disrupted connectivity, and how these complex alterations may contribute to motor and cognitive decline. In this context, our study leverages multimodal neuroimaging to elucidate relationships among clinical symptoms, cognitive performance, and brain alterations in early-stage PD. We hypothesize that even at early stages, when motor symptoms are subtle, measurable changes in grey and WM integrity are already present and contribute to motor and cognitive dysfunction. Furthermore, identifying and mapping these alterations may help develop robust progression biomarkers, integrating structural changes with clinical trajectories to optimize early management. Methods The study followed the Declaration of Helsinki and was approved by the Institutional Review Board of Hospital Clinica Benidorm. All participants provided written informed consent. Participants and clinical assessment The study’s size was determined by the availability of eligible participants. To evaluate adequacy, we performed a power analysis using G*Power (v3.1.9.7). Because multimodal MRI studies showed moderate-to-large group differences, 5 , 10 we considered Cohen’s d = .80 a reasonable effect size estimate. Under these parameters (two-tailed test, α = .05, power = 0.80), 25 participants per group were required. Our final sample of 31 PD patients and 30 healthy controls (HC) met this threshold. Participants were recruited from the Neurology Department at Hospital Clínica Benidorm (September 2015-October 2016). An experienced neurologist conducted full physical and cognitive assessments. Parkinson’s disease patients met the UK Parkinson Disease Brain Bank criteria, had Hoehn-Yahr stage I-II, and Mini-Mental State Examination (MMSE) ≥ 24. Healthy controls were matched for age, gender, and education, and shared similar socio-cultural background ensuring group comparability. Eligibility relied on clinical history, cognitive testing, and physical examination. Exclusion criteria included psychiatric, neurological, or systemic diseases; prior head trauma with loss of consciousness; MRI-incompatible implants; musculoskeletal or joint conditions; substance abuse; and claustrophobia. Collected data included demographic variables (age, education, medications), clinical scores, UPDRS (with subscales), Hoehn-Yahr stage, MMSE, Symbol Digit Modalities Test (SDMT), and Free and Cued Selective Reminding Test (FCSRT). Neuroimaging and clinical evaluations were performed within the same week using standardized protocols. Magnetic resonance imaging MRI data were acquired on a Philips Achieva 3 T Series-X system (Philips Healthcare, The Netherlands) with a SENSE neurovascular coil. The protocol included a high-resolution axial 3D T1-weighted sequence (160 slices; 1-mm isotropic voxels; FOV 250 × 250 mm 2 ; TR = 13 ms; TE = 7.3 ms) and DTI sequence with 32 non-collinear directions, b = 1000 s/mm 2 , 2 b0 images, voxel size 2 × 2 × 2 mm³, TR = 6499 ms, TE = 70 ms, and SENSE factor 1.9. All MRI datasets were examined to ensure the absence of artefacts or anatomical abnormalities. Voxel-based morphometry analysis We used SPM12 software for VBM processing. Pre-processing included setting the origin at the anterior commissure, segmentation into grey matter (GM) and WM, normalization using DARTEL, and smoothing with an 8-mm full-width-at-half-maximum Gaussian kernel. All analyses included age, sex, and intracranial volume as covariates. Between-group GM differences were assessed using a 2-sample t -test with a voxel-wise threshold of P < .001 (uncorrected), followed by cluster-level family-wise error (FWE) correction at P < .05. Multiple regression analyses were performed to examine associations between GM volume and clinical or cognitive variables. For the regression models, statistical significance was determined using FDR <.05. DTI analyses We conducted tract-based spatial statistics (TBSS) with FSL v5.0 (FMRIB, Oxford, UK) for voxel-wise statistical analysis. Pre-processing included correction for head-motion and eddy-current distortion, and diffusion tensor fitting with FMRIB’s Diffusion Toolbox (FDT v.3.0). The mean fractional anisotropy (FA) skeleton was generated following FA map registration, and aligned to the average space as input for TBSS. Voxel-wise statistics were performed using the randomize tool with 10 000 permutations and a threshold of 0.2, with age, gender, and ICV as covariates. Family-wise error corrected maps were obtained with a P < .05. Following TBSS analysis, WM regions with significant differences between the groups were identified using a probabilistic digital atlas 11 and selected for tractography. For tractography pre-processing and fibre tract generation we used ExploreDTI. 12 After eddy-current and head-motion correction, DTI estimation was conducted using a non-linear least square approach. 13 Diffusion tensor imaging scalar maps, including FA and mean diffusivity (MD), were computed. Whole-brain tractography was performed by selecting all seed voxels with FA > 0.2. Streamlines were propagated using Euler integration 14 and a tractography algorithm step size of 1 mm. The whole-brain tractography data were imported into TrackVis software v.0.6. 15 Tracts dissections were conducted using the 2 regions-of-interest approach based on published atlases. 16 Reconstructions of the corpus callosum , corticospinal tracts, cingulate fasciculi, uncinate fasciculi, inferior fronto-occipital fasciculi, and arcuate fasciculi were performed. The corpus callosum was segmented into the seven subdivisions ( Figure 1 ) defined by Witelson (orbitofrontal, anterior frontal, superior frontal, superior parietal, posterior parietal, temporal, and occipital). 17 , 18 Figure 1. Open in a new tab (A) Illustrative reconstruction of the corpus callosum in a 60-year-old healthy male control, with scalar index of factional anisotropy. (B) Reconstruction of the same corpus callosum with colours representing a seven-segment tractography-based division according to the probabilistic atlas of the corpus callosum . The segments are: orbital frontal (red), anterior frontal (yellow), superior frontal (orange), superior parietal (green), posterior parietal (blue), temporal (violet), and occipital (purple). For connectivity-based parcellation of caudate nuclei with cortical areas we used FSL v.5.0. Cortical target masks comprising the anterior and posterior divisions of the cingulate cortex, frontal medial cortex, middle frontal gyrus, orbital frontal cortex, frontal pole, inferior frontal gyrus pars triangularis, middle frontal gyrus, paracingulate gyrus and subcallosal cortex, were obtained from the Harvard Oxford subcortical and MNI structural atlas in FSL. All masks were thresholded to exclude WM and binarized. Individual caudate segmentations derived from structural images were used as seed masks. Diffusion tensor imaging scalar maps were registered to the standard space with FMRIB’s FLIRT linear registration tool. Probabilistic tractography was performed using ProbtrackX 19 (5000 samples per voxel, step length 0.5 mm, curvature threshold 0.2, distance correction option enabled). Connectivity was evaluated separately for each hemisphere between the caudate nucleus (seed) and its ipsilateral cortical targets (terminations). Voxel-wise classification maps were thresholded to retain only connections with a probability ≥50% and binarized. For each caudate-cortex connection, FA, MD, streamlines, and volumes normalized to ICV were determined. 20 Statistical analyses We used SPSS v24.0 (IBM, United States). Age and sex differences were assessed using independent t -tests and chi-square tests, respectively. Quantitative variables are presented as mean ± SD or median (range), as appropriate. Normality was assessed with the Kolmogorov-Smirnov test. SDMT scores were converted to z -scores. 21 Between-group comparisons were performed using the Mann-Whitney U test or unpaired t -test, depending on data distribution. Associations between variables were explored using partial correlation tests (Pearson or Spearman, depending on data distribution), controlling for age, sex, and years of education as covariates. All P -values obtained from multiple tests were adjusted using the false discovery rate (FDR) correction (Benjamini-Hochberg procedure). 22 Statistical significance was set at P < .05 (FDR corrected). No missing data were observed. No additional analyses such as subgroup, interaction, or sensitivity analyses were performed beyond the primary statistical models described. Use of AI tools We used Google’s Gemini to refine the clarity and flow of the text. The tool did not generate content, data analyses, or images. All suggestions were reviewed and edited by the authors, who take full responsibility for the final article. Results Among PD patients, 14 received dopamine agonists (45%), 23 levodopa (75%), and 17 MAO-B inhibitors (15%). No significant group differences were found in sex, age, education, or handedness ( Table 1 ). Parkinson’s disease patients had a mean disease duration of 5.3 years and median onset age of 61. While global MMSE was comparable, PD patients exhibited lower MMSE recall ( P -FDR = .021), FCSRT total recall ( P -FDR = .014), FCSRT delayed total recall ( P -FDR = .021), and z -SDMT scores ( P -FDR = .025). Parkinson’s disease patients showed increased CSF volume ( P -FDR = .03), lower brain parenchymal fraction ( P -FDR = .025); and reduced left and right caudate volumes ( P -FDR = .024; P -FDR = .026, respectively). Table 1. Demographic, clinical, and MRI characteristics of the subjects enrolled in the study. PD ( n = 31) HC ( n = 30) P P -FDR Sex (M/F) (number) (25/6) (24/6) .72 .79 Age, a years (range) 66 (65-69) 63 (60-67) .23 .3 Disease duration, b years (SD) 5.3 ± 3.2 n.a . Age at diagnosis a (range) 61 (56-64) n.a. Handedness R/L 31/0 29/1 .81 .81 Education, a years 14 (10-19) 14 (11-19) .72 .79 HY scale a 1 (1-2) n.a UPDRS a (range) 17.5 (14-31) n.a . UPDRS-I a (range) 3 (0-5) n.a UPDRS-II a (range) 9 (4-16) n.a . UPDRS-III a (range) 8 (4-16) n. a UPDRS-IV a (range) 1(0-4) n.a . MMSE a (range) 29 (28-30) 30 (29-30) .07 .12 MMSE orientation a (range) 10 (7-10) 10 (9-10) .15 .22 MMSE registration a (range) 3 (2-3) 3 (2-3) .32 .39 MMSE attention and calculation a (range) 5 (4-5) 5 (4-5) .55 .64 MMSE recall a (range) 2 (0-3) 3 (2-3) .001 .021 c MMSE language a (range) 9 (8-9) 9 (8-9) .16 .22 FCSRT total free recall b 24.78 ± 4.35 27.47 ± 4.91 .083 .13 FCSRT total recall b 38.26 ± 2.86 41.10 ± 2.25 .002 .014 c FCSRT delayed free recall b 8.89 ± 1.96 10.26 ± 1.73 .029 .07 FCSRT delayed total recall b 10.84 ± 1.72 13.15 ± 1.3 .001 .021 c z-SDMT b 29.22 ± 13.5 41.07 ± 10.72 .006 .025 c GMV b (mL) 374.96 ± 21 389.77 ± 21 .04 .084 WMV b (mL) 353.65 ± 35 371.27 ± 18 .06 .11 CSFV b (mL) 271.39 ± 36 238.95 ± 34 .01 .03 c BPF b 0.73 ± 0.03 0.76 ± 0.03 .006 .025 c L_Caudate b (mL) 2.11 ± 0.4 2.77 ± 0.4 .005 .024 c R_Caudate b (mL) 2.33 ± 0.5 2.82 ± 0.5 .007 .026 c Open in a new tab P- values are comparisons between the patient and control groups. a Data are median. b Data are means ± SD. c Significance after FDR correction. Abbreviations: BPF = brain parenchymal fraction, CSF = cerebrospinal fluid, F = female, FCSRT = Free and Cued Selective Reminding Test, GMV = grey matter volume, HC = healthy controls, HY = Hoehn and Yahr scale, L = left, M = male, MMSE = Mini-Mental State Examination, n.a. = not applicable, PD = Parkinson’s disease patients, R = right, SDMT = Symbol Digit Modalities Test, UPDRS = Unified Parkinson’s Disease Rating Scale, WMV = white matter volume. Voxel-based morphometry Parkinson’s disease patients showed significant GM reduction in the caudate nuclei and left BA18 ( Figure 2 ). The reverse comparison (PD > controls) showed no significant voxels. Multiple regression analyses in the PD group revealed an association between right BA21 and MMSE scores ( Figure 3 ). No other significant associations were found. Figure 2. Open in a new tab Between-group differences in grey matter volume displayed on the T1 MNI template ( z -plane). Yellow clusters indicate regions of decreased grey matter volume in the Parkinson’s disease group compared with controls. The colour bar represents T -values. Statistical threshold: P < .001 voxel-wise (uncorrected), cluster-level FWE corrected at P < .05. Figure 3. Open in a new tab Multiple regression analysis with voxel-based morphometry showing GM correlated with MMSE scores, overlaid on the z -plane of the T1 MNI average brain. The colour bar represents T -values. Significance: P < .05, FDR corrected. Diffusion tensor imaging Tract-based spatial statistics showed significantly lower FA in in several WM tracts in PD ( Figure 4 ). No increases in FA values were observed compared to controls. Manual dissections of WM tracts revealed significant differences in diffusion metrics across fascicles ( Table 2 ). Figure 4. Open in a new tab TBSS results showing FA differences between PD patients and controls. The red-yellow scale regions indicate areas with reduced fractional anisotropy values in the PD group (FWE corrected P < .05). The thresholded stat image has been thickened for better perception. Unc = uncinate fasciculus, IFO = inferior fronto-occipital fasciculus, CC = corpus callosum, CST = corticospinal tract, Cg = cingulate fasciculus, ATR = anterior thalamic radiation, Arc = arcuate fasciculus. Table 2. Tract-specific result comparisons after manual dissections of the white matter tracts between the PD and HC groups. PD patients Controls P P -FDR L-Uncinate FA 0.36 ± 0.03 0.42 ± 0.03 <.001 .0004 R-Uncinate FA 0.36 ± 0.04 0.43 ± 0.04 <.001 .0004 R-Uncinate MD 0.87 ± 0.04 0.82 ± 0.04 .005 .01 R-Uncinate_RD 0.7 ± 0.04 0.62 ± 0.06 .003 .008 L-IFO FA 0.42 ± 0.03 0.48 ± 0.04 .001 .0028 L-IFO Str 1154 ± 380 1688 ± 468 .003 .007 R- IFO FA 0.41 ± 0.02 0.49 ± 0.02 <.001 <.001 R-IFO MD 0.87 ± 0.03 0.82 ± 0.04 .004 .009 R- IFO RD 0.67 ± 0.04 0.59 ± 0.07 .003 .008 R-Cingulum FA 0.43 ± 0.03 0.48 ± 0.04 .005 .01 R-Cingulum Str 886 ± 237 1412 ± 377 <.001 .0013 L-CST FA 0.46 ± 0.03 0.49 ± 0.03 .023 .04 R-CST Str 1944 ± 553 2608 ± 891 .028 .049 CC-OF MD 1.07 ± 0.16 0.87 ± 0.08 <.001 .0011 CC-OF RD 0.73 ± 0.13 0.61 ± 0.08 .004 .009 CC-OF Str 412 ± 512 1195 ± 514 <.001 .0013 CC-AF MD 1.13 ± 0.25 0.85 ± 0.06 <.001 .0011 CC-AF RD 0.79 ± 0.21 0.64 ± 0.1 .011 .022 CC-AF Str 513 ± 648 1454 ± 382 <.001 .0005 CC-SF MD 1.25 ± 0.3 0.86 ± 0.1 <.001 .0005 CC-SF RD 0.91 ± 0.26 0.61 ± 0.14 <.001 .0015 CC-SF Str 900 ± 755 2468 ± 869 <.001 .0018 CC-SP FA 0.48 ± 0.09 0.55 ± 0.04 .023 .043 CC-SP MD 1.26 ± 0.41 0.85 ± 0.08 .001 .003 CC-SP RD 0.92 ± 0.4 0.56 ± 0.07 .001 .004 CC-SP STR 894 ± 595 1947 ± 592 <.001 .0007 CC-PP MD 1.16 ± 0.26 0.86 ± 0.07 <.001 .0007 CC-PP RD 0.81 ± 0.25 0.57 ± 0.12 .001 .004 CC-PP Str 456 ± 384 1512 ± 648 <.001 .0004 CC-OCC FA 0.53 ± 0.06 0.61 ± 0.05 .001 .0028 CC-OCC MD 1.03 ± 0.13 0.87 ± 0.07 .001 .0013 CC-OCC RD 0.7 ± 0.15 0.54 ± 0.11 .002 .0046 CC-OCC Str 1031 ± 858 2292 ± 716 .001 .0006 CC-TEM FA 0.51 ± 0.05 0.56 ± 0.04 .018 .035 CC-TEM MD 1.066 ± 0.18 0.9 ± 0.07 .003 .007 CC- TEM RD 0.75 ± 0.16 0.59 ± 0.08 .003 .007 R-ARC MD 0.8 ± 0.06 0.75 ± 0.03 .026 .047 R-ARC RD 0.63 ± 0.05 0.55 ± 0.03 <.001 .0005 R-ARC Str 1226 ± 406 1762 ± 510 .005 .01 Open in a new tab P- values are an unpaired 2-tailed test between the groups. P-FDR=significance after FDR correction. L and R are left and right hemisphere, respectively. Only significant results are shown. Abbreviations: AF = anterior frontal, ARC = Arcuate fasciculus, CC = Corpus callosum, CST = Corticospinal tract, IFO = inferior fronto-occipital fasciculus, OCC = occipital, OF = orbital frontal, PP = posterior parietal, SF = superior frontal, SP = superior parietal, Str = Streamlines, TEM = temporal. Cortical connectivity of the caudate nuclei Parkinson’s disease patients exhibited significant increases in MD values in several caudate-cortical projections related to the frontal lobes and paracingulate gyri of both hemispheres ( Table 3 ). No other significant differences were observed. Table 3. Mean and SD of MD values (mm 2 /s) of the segmented caudate-cortical projections. PD Controls P P -FDR L_frontal medial cortex MD 0.0017 ± 0.0001 0.0013 ± 0.00009 .003 .028 L_frontal orbital cortex MD 0.0017 ± 0.0004 0.0013 ± 0.0003 .003 .028 L_frontal pole MD 0.0017 ± 0.0004 0.0013 ± 0.0004 .002 .028 L_inferior frontal gyrus pars triangularis MD 0.0017 ± 0.0005 0.0012 ± 0.0003 .006 .043 L_orbital_cortex MD 0.0017 ± 0.0004 0.0013 ± 0.0003 .003 .024 L_paracingulate gyrus MD 0.0017 ± 0.0004 0.0014 ± 0.0003 .007 .043 L_subcallosal MD 0.0018 ± 0.0004 0.0014 ± 0.0003 .009 .046 L_superior frontal gyrus MD 0.0018 ± 0.0004 0.0014 ± 0.0003 .007 .043 R_frontal medial cortex MD 0.0017 ± 0.0004 0.0013 ± 0.0003 .003 .028 R_frontal orbital cortex MD 0.0017 ± 0.0004 0.0012 ± 0.0004 .003 .028 R_frontal pole MD 0.0017 ± 0.0004 0.0012 ± 0.0004 .002 .028 R_orbital cortex MD 0.0017 ± 0.0004 0.0012 ± 0.0004 .003 .028 Open in a new tab P- values are unpaired two-tailed test comparisons between the PD patients and control groups. P-FDR: significance after FDR correction. Only significant results are shown. Abbreviations: L = left hemisphere, R = right hemisphere. Correlation analyses Disease duration Disease duration correlated negatively with streamline counts in the superior ( r = −0.79, P -FDR = .02) and posterior parietal ( r = −0.85, P -FDR = .004) corpus callosum segments. Motor severity Hoehn and Yahr scale scores showed negative correlations with FA in occipital ( ρ = −0.81, P -FDR = .0039) and temporal ( ρ = −0.74, P -FDR = .021) callosal streamlines. Total UPDRS scores showed similar negative correlations with FA in these callosal regions (occipital: ρ = −0.85, P -FDR = .002; temporal: ρ = −0.85, P -FDR = .0017) and with streamline counts connecting the left caudate to the left frontal pole ( ρ = −0.68, P -FDR = .042). Functional disability The UPDRS-II showed negative correlations with FA in the occipital ( ρ = −0.89, P -FDR = .0004) and temporal ( ρ = −0.86, P -FDR = .0017) streamlines of the corpus callosum. Additionally, streamline counts between the left caudate and both the frontal pole ( ρ = −0.76, P -FDR = .035) and left superior frontal gyrus ( ρ = −0.75, P -FDR = .04). Motor complications UPDRS-IV correlated positively with RD in the anterior frontal segment of the corpus callosum ( ρ = 0.75, P -FDR = .049). Cognitive outcomes Episodic memory performance, measured by the total FCSRT, correlated negatively with MD in the right uncinate fasciculus ( r = −0.86, P -FDR = .0026) and with RD in the superior parietal corpus callosum ( r = −0.75, P -FDR = .048). FCSRT Delayed Total Recall scores positively correlated with streamlines counts between the caudate to frontal regions bilaterally. Specifically, streamline counts between the left caudate and the frontal orbital cortex ( r = 0.86, P -FDR = .0019) and the left orbital cortex ( r = 0.86, P -FDR = .0019) were highly associated with better delayed recall performance. In the right hemisphere, significant correlations were also observed with the frontal operculum ( r = 0.84, P -FDR = .004) and the frontal orbital cortex ( r = 0.77, P -FDR = .028). Mini-Mental State Examination attention and calculation scores showed positive correlations with streamlines linking the right caudate and both anterior ( ρ = 0.74, P -FDR = .04) and posterior ( ρ = 0.78, P -FDR = .018) cingulate divisions (see Table 4 ). Table 4. Correlation analyses between MRI measures and clinical/cognitive variables. WM tract Metric r / ρ P P -FDR Disease duration (years) Corpus callosum (superior parietal) Streamlines −0.79 .0012 .02 Corpus callosum (posterior parietal) Streamlines −0.85 .0002 .004 Hoehn & Yahr Corpus callosum (occipital) FA −0.81 .0007 .0039 Corpus callosum (temporal) FA −0.74 .004 .021 UPDRS Corpus callosum (occipital) FA −0.846 .0002 .002 Corpus callosum (temporal) FA −0.85 .0002 .0017 Caudate-frontal pole (left hemisphere) Streamlines −0.68 .0098 .042 UPDRSII Corpus callosum (temporal) FA −0.86 .00016 .0017 Corpus callosum (occipital) FA −0.89 .00003 .0004 Caudate-frontal pole (left hemisphere) Streamlines −0.76 .0027 .035 Caudate-superior frontal gyrus (left hemisphere) Streamlines −0.75 .003 .04 UPDRS-IV Corpus callosum (anterior frontal) RD 0.75 .003 .049 FCSRT total recall Right uncinate MD −0.86 .0001 .0026 Corpus callosum (superior parietal) RD −0.75 .003 .048 FCSRT delayed total recall Caudate-frontal orbital cortex (left hemisphere) Streamlines 0.86 .00015 .0019 Caudate-orbital cortex (left hemisphere) Streamlines 0.86 .00015 .0019 Caudate-frontal operculum (right hemisphere) Streamlines 0.84 .00033 .004 Caudate-frontal orbital cortex (right hemisphere) Streamlines 0.77 .002 .028 MMSE attention and calculus Caudate-cigulate anterior division (right hemisphere) Streamlines 0.74 .0034 .04 Caudate-cigulate posterior division (right hemisphere) Streamlines 0.78 .0014 .018 Open in a new tab Correlation analyses between MRI measures and clinical/cognitive variables. P-FDR= p values after FDR correction. Only significant results are shown. Abbreviations: FA = fractional anisotropy, FCSRT = Free and Cued Selective Reminding Test, MD = mean diffusivity, MMSE = Mini-Mental State Examination, RD = radial diffusivity. Discussion Our study suggests that early-stage PD patients present subtle yet specific cognitive impairments in memory, attention, and processing speed, despite preserved global cognition on the MMSE. These findings are consistent with current models proposing that cognitive vulnerability may appear early in the disease course, even when motor symptoms remain mild, aligning with increasing evidence of early multisystem nature of PD. 2 , 23 Consistent with prior research, we found significant GM reductions in the caudate nuclei of early-stage PD patients. 24 , 25 Our results align with recent reports suggesting reduced volume of bilateral caudate nuclei at baseline 26 and a stage-specific pattern with early atrophy mainly affecting the left caudate. 27 Given the caudate’s role in executive and motor control, 28 these structural changes may represent early disease vulnerability secondary to nigrostriatal degeneration. 8 , 29 Besides, this pattern is consistent with the Braak staging model, which proposes initial subcortical involvement with subsequent progression to high‐order sensory association areas and the prefrontal cortex via anatomically coupled networks. 30 Complementing these structural findings, the association between MMSE scores and the right Brodmann Area 21 (BA21) suggests that temporal associative regions may also contribute to early cognitive changes. This involvement likely reflects alterations in networks supporting semantic and integrative processing, with established connections to limbic and frontal areas. 31 Thus, BA21 may serve as a potential imaging marker for early cognitive dysfunction in non-demented PD patients. 32 While PD has traditionally conceptualized as a primarily a GM disorder, our results support increasing evidence of significant WM microstructural alterations 33 even from early stages. 34 , 35 Neuroinflammation is a key factor in PD, and the association between WM damage and systemic inflammation in PD has been demonstrated in previous studies. 36 Our findings further support the hypothesis that widespread WM impairment emerges early in PD, potentially driven by synapsin and Lewy body aggregation in vulnerable regions, leading to atrophy, neuron loss, and demyelination. A central component of these WM alterations involves fronto-striatal pathways. The observed increase in MD within caudate projections towards frontal, prefrontal, and cingulate regions suggests an early compromise of fronto-striatal connectivity, potentially reflecting dopaminergic depletion and axonal vulnerability. 37 Given the sensitivity of MD to subtle microstructural WM changes, 38 these results suggest early connectivity impairment in circuits fundamental for executive and motor control, 28 providing a plausible pathophysiological basis for the cognitive and motor deficits observed in our cohort. 39 According to our results, cognitive and clinical symptoms may relate not to caudate atrophy itself but rather to compromised connectivity between the caudate nuclei and frontal and cingulate cortices. As PD progresses, dopaminergic depletion may further disrupt these networks, potentially leading to a worsening of the associated cognitive and clinical symptoms. Whether caudate atrophy reflects retrograde degeneration within fronto-striatal circuits or, conversely, precedes and contributes to subsequent WM impairment remains unclear and warrants further study. The functional relevance of these structural and microstructural alterations are further supported by tract-specific correlations. Reduced FA and streamline counts between the left caudate and the frontal pole and superior frontal gyrus correlated with greater functional disability (UPDRS-II), underscoring the contribution of fronto-striatal networks to executive functioning and daily activities. 40 Similarly, episodic memory (FCSRT delayed recall) correlated with streamline counts between the caudate and bilateral frontal regions, including orbital and opercular cortices, reinforcing the well-established role of fronto-striatal circuits in memory retrieval and strategic encoding. 41 Positive correlations between MMSE scores and caudate-cingulate connectivity, specifically to both anterior and posterior divisions of the right cingulate cortex, emphasizes the importance of caudate-fronto-limbic circuits for attentional control, motivation, and cognitive flexibility, 42 and is consistent with previous reports linking cingulate dysfunction to cognitive impairment in PD. 43 Beyond fronto-striatal pathways, interhemispheric WM integrity also appears compromised early in PD. Correlations between disease duration and reduced streamline counts in superior and posterior parietal segments point to early interhemispheric impairment, particularly in regions supporting visuospatial and sensorimotor integration. This pattern suggests a selective, rather than diffuse, impairment of callosal fibres, aligning with findings suggesting early and progressively deteriorating microstructural integrity of the corpus callosum. 44 , 45 Similarly, negative associations between motor severity and FA in temporal and occipital callosal fibres highlight the role of interhemispheric connectivity in motor control. Reduced FA in these fibres likely reflects axonal loss or demyelination, disrupting communication within motor networks and contributing to characteristic symptoms such as bradykinesia and gait disturbances. 45 Furthermore, motor complications (UPDRS-IV) correlated with increased RD in the anterior frontal segment of the corpus callosum, likely reflecting demlyenination or axonal degeneration and corresponding to more advanced motor symptomatology. These findings suggest the importance of corpus callosum integrity for efficient interhemispheric communication and coordination in complex motor tasks, 46 and are in in line with previous studies linking WM impairment to motor deficits in PD and other neurodegenerative disorders. 47 , 48 Finally the observed association between FCSRT scores and RD in the posterior parietal callosal fibres is consistent with studies linking posterior callosal degeneration to cognitive dysfunction, detectable even in early PD and exacerbating with disease progression 44 , 45 Overall, our results suggest that CC involvement is an early event in PD pathology, contributing to cognitive and motor dysfunction, impairing interhemispheric communication. This selective callosal vulnerability may constitute an early biomarker of disease evolution, reflecting ongoing neurodegenerative processes prior to detectable cortical atrophy. Finally, episodic memory deficits, measured by total FCSRT scores, correlated with increased MD in the right uncinate fasciculus. The UNC connects the orbitofrontal cortex, BA10 and temporal lobes; and plays a role in episodic memory and language, functions that are necessary to perform the FCSRT. 49 This tract is often impaired in PD and has been linked to cognitive impairment in PD, 50 providing a plausible pathophysiological basis for the observed cognitive findings. All in all, our results underscore the dual involvement of fronto-striatal connectivity and interhemispheric pathways in the early clinical manifestations of PD. Microstructural CC alterations appear to mediate both motor and cognitive dysfunction, while caudate-frontal and caudate-cingulate connections seem particularly relevant for executive and memory-related deficits. The convergence of these findings supports their potential utility as neuroimaging biomarkers for early detection, monitoring, and therapeutic strategies in PD. Our study presents some limitations that warrant consideration. The relatively small sample size may limit the generalizability of our findings and could reduce sensitivity to detect subtle effects. Additionally, all patients in our cohort were under dopaminergic treatment, which may have influenced clinical-imaging associations. In particular, treatment-naïve patients might show stronger correlations between motor impairment and structural imaging markers, as their symptoms would more directly reflect underlying neural pathology. Future research with larger, longitudinal cohorts including treatment-naïve patients, will be essential to validate these findings and clarify the impact of medication on clinical-structural relationships. In conclusion, our study highlights the importance of WM microstructural integrity, particularly fronto-striatal and interhemispheric pathways, for cognitive and motor functions in early-stage PD. Progressive disruption of these networks may underlie clinical heterogeneity and disease progression. Specific WM tracts including occipital, parietal and temporal callosal fibres, caudate-orbitofrontal connections, and the uncinate fasciculus, emerge as promising neuroimaging biomarkers for early detection and monitoring of PD-related dysfunction. These alterations may be present in the initial stages of the disease and remain undetectable in conventional MRI protocols. Preservation of these networks may constitute a target for interventions aimed at slowing disease progression and improving the quality of life in PD. Acknowledgements The authors would like to express their gratitude to the study participants for their generosity, time, and cooperation. We also extend our sincere appreciation to our MR technologists, Mr. Jonatan Monge Yvars, and Mr. Enrique García Rodríguez, for their exceptional technical support during image acquisition, and Mr. Alex Finestrat Mostazo for his excellent technical support in the preparation of MR data prior to analysis. Contributor Information Ángela Bernabéu-Sanz, Magnetic Resonance Department, Inscanner SL, Calle San Pedro Poveda 10, Alicante, CP 03010, Spain; Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain. Sandra Morales, Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain. Valery Naranjo, Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València (UPV), Camino de Vera s/n, Valencia, CP 46022, Spain. Eduardo Fernández, Instituto de Bioingeniería, Universidad Miguel Hernández de Elche, Avenida de la Universidad s/n, CP 03202 Elche, Spain; CIBER-BBN, Avenida Monforte de Lemos 3-5, Pabellón 11, Planta 0, Madrid, CP 28029. Funding This study was funded by the Centro para el Desarrollo Tecnológico Industrial (CDTI) through the BRAIM project (IDI-20130020) and was partially supported by grants PDC2022-133952-I00 and PID2022-141606OB-I00 from the Spanish Ministerio de Ciencia, Innovación y Universidades, as well as by grant CIPROM/2023/25 from the Generalitat Valenciana. Funding for open access charge was provided by CRUE-Universitat Politècnica de València. Conflicts of interest The authors declare no conflicts of interest to disclose. References 1. Simon DK, Tanner CM, Brundin P. Parkinson disease epidemiology, pathology, genetics, and pathophysiology. Clin Geriatr Med. 2020;36:1-12. 10.1016/j.cger.2019.08.002 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 2. 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Biology (Basel). 2023;12:1-31. 10.3390/biology12030475 [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Articles from The British Journal of Radiology are provided here courtesy of Oxford University Press ACTIONS View on publisher site PDF (1.4 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top