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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Med . 2026 Mar 6;24:241. doi: 10.1186/s12916-026-04770-7 Search in PMC Search in PubMed View in NLM Catalog Add to search Impairment of brain short association fibers across clinical stages in amyotrophic lateral sclerosis: a new biomarker mirroring disease progression Nao-Xin Huang Nao-Xin Huang 1 Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Nao-Xin Huang 1, # , Zi-Wei Cai Zi-Wei Cai 1 Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Zi-Wei Cai 1, # , Shao-Peng Zhuang Shao-Peng Zhuang 1 Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Shao-Peng Zhuang 1, # , Hong-Yu Lin Hong-Yu Lin 2 School of Medical Imaging, Fujian Medical University, Fuzhou, 350122 China Find articles by Hong-Yu Lin 2 , Sheng Chen Sheng Chen 3 Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Sheng Chen 3, ✉ , Zhang-Yu Zou Zhang-Yu Zou 3 Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Zhang-Yu Zou 3, ✉ , Ye Wu Ye Wu 4 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China Find articles by Ye Wu 4, ✉ , Hua-Jun Chen Hua-Jun Chen 1 Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China Find articles by Hua-Jun Chen 1, ✉ Author information Article notes Copyright and License information 1 Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001 China 2 School of Medical Imaging, Fujian Medical University, Fuzhou, 350122 China 3 Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, 350001 China 4 School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 China ✉ Corresponding author. # Contributed equally. Received 2025 Nov 1; Accepted 2026 Mar 3; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13077863 PMID: 41792723 Abstract Background A quantitative biomarker for clinical staging is essential for amyotrophic lateral sclerosis (ALS) stratification. This study evaluated microstructural impairment in brain short association fibers (SAFs) across ALS stages via neurite orientation dispersion and density imaging (NODDI) and assessed correlations with disease severity. Methods Diffusion-weighted imaging data were collected from 87 ALS patients (categorized into four groups King's stages) and 37 healthy controls. Whole-brain SAF mapping was performed via a spherical deconvolution-driven probabilistic tractography approach. Diffusion tensor imaging (DTI) and NODDI parameters (neurite density index, NDI; orientation dispersion index, ODI; isotropic volume fraction, ISO) were estimated for each SAF. Results Seven SAFs connecting the left postcentral-precentral gyrus, left precentral-precentral gyrus, right postcentral-precentral gyrus, right paracentral-posterior cingulate gyrus, left paracentral-posterior cingulate gyrus, left precentral-superior parietal gyrus, and left precentral-superior frontal gyrus exhibited significant NDI differences across the five groups. Additionally, one fiber connecting the left medial orbitofrontal-rostral anterior cingulate gyrus demonstrated an ISO difference [false discovery rate (FDR)-corrected p < 0.05]. Progressive trends of NDI reduction and ISO increase were observed at higher ALS stages. No intergroup differences were found in the ODI or DTI parameters. The NDI values of these seven SAFs were positively correlated with disease severity scores (FDR-corrected p < 0.05). Combining NDI and ISO revealed moderate classification potential for ALS (area under the curve = 0.780). Conclusions Neurite injury in SAFs involving primary motor and extramotor areas worsened alongside clinical staging and motor disability in ALS. NODDI provides quantitative SAF-related biomarkers for assessing ALS disease severity. Supplementary Information The online version contains supplementary material available at 10.1186/s12916-026-04770-7. Keywords: Amyotrophic lateral sclerosis, Short association fiber, Microstructural impairment, Neurite orientation dispersion and density imaging, Clinical stages Background Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disorder characterized by the selective death of both upper and lower motor neurons, with a median survival time of 3 years following the initial onset of symptoms [ 1 ]. Its clinical heterogeneity and phenotypic overlap with other neurological disorders often delay accurate diagnosis [ 1 ]. As timely interventions can slow the progression of ALS, early diagnosis during the disease course can improve outcomes [ 1 ]. The King’s clinical staging system, by categorizing disease burden based on functional involvement, provides a validated framework to assess disease severity and track its theoretical progression continuum [ 2 , 3 ]. Neuroimaging studies have identified early cerebral alterations in ALS, suggesting potential biomarkers of pathology [ 4 , 5 ]. Investigating microstructural changes across these defined clinical stages can therefore elucidate the pathophysiology of disease spread and identify biomarkers closely linked to clinical severity. Diffusion magnetic resonance imaging (dMRI) is a noninvasive neuroimaging technique that measures water diffusion patterns to characterize white matter (WM) pathology in ALS. For example, diffusion tensor imaging (DTI) quantifies anisotropic diffusion by evaluating the direction and magnitude of water molecular diffusion in three-dimensional space [ 6 ]; the two most common DTI parameters are fractional anisotropy (FA) and mean diffusivity (MD). Previous DTI studies on ALS have consistently indicated that decreased FA and increased MD of the corticospinal tract are associated with greater disease severity and a faster rate of disease progression [ 7 , 8 ]. However, DTI’s fundamental assumption of Gaussian diffusion and simplified single-compartment model limit its ability to characterize complex WM microstructures, reducing its sensitivity and specificity for detecting subtle pathological changes [ 9 , 10 ]. Compared with conventional DTI, higher-order dMRI models enable the further characterization of WM abnormalities in ALS. For example, diffusion kurtosis imaging (DKI) revealed a reduction in WM microstructure complexity in ALS patients involving the corticospinal tract, middle corpus callosum, occipital lobe, and superior parietal lobule [ 9 ]. Neurite orientation dispersion and density imaging (NODDI), as a multicompartment biophysical diffusion model, provides a quantitative depiction of neurite morphological characteristics [ 10 ] with biologically meaningful metrics, such as the neurite density index (NDI) and orientation dispersion index (ODI), which reflect the axon density and the degree of dispersion of the fiber orientations, respectively [ 10 ]. A series of NODDI studies revealed the abnormal microscopic morphology of axons in ALS patients, which involves both motor-related areas and extramotor regions and contributes to motor dysfunction and nonmotor deficits (e.g., cognitive impairment) [ 11 – 13 ]. These advanced dMRI techniques provide better detection of ALS-related WM abnormalities and more precise quantification of disease progression [ 11 – 13 ]. Neuroimaging evidence based on dMRI data has consistently demonstrated that ALS is characterized by widespread structural disconnection. For example, previous NODDI and DKI studies have revealed ALS-related WM damage, primarily involving the corticospinal tract and corpus callosum, indicating top-down and interhemispheric structural disconnection [ 11 – 13 ]. In addition, structural brain network analysis based on DTI data has revealed subnetwork disconnection centered in bilateral primary motor regions, which serve as one of the most crucial pathological neural circuits in ALS [ 14 ]. Moreover, abnormalities in the topological properties (attributed to structural disconnection) of the whole-brain structural network (constructed via diffusion-based tractography) are also essential features of ALS [ 15 ]. The severity and topographic distribution of whole-brain network structural disconnection can reflect the progression of ALS [ 14 , 15 ]. Notably, the existing diffusion-related studies on ALS have focused mainly on the detection of deep white matter (DWM) microstructure damage, which involves projection fibers (i.e., ascending and descending fibers, such as the corticospinal tract [ 16 ] and thalamocortical fibers [ 8 ]), commissural fibers (i.e., fibers that cross the midline and connect functionally identical parts of the two hemispheres, such as the corpus callosum [ 17 ]), and long association fibers (i.e., fibers that connect distant regions in the same cerebral hemisphere, such as the superior longitudinal fasciculus [ 6 ] and cingulum [ 18 ]). In contrast, short association fibers (SAFs) are cortico-cortical U-shaped fibers connecting adjacent gyri within superficial white matter (SWM) [ 19 ]. SAFs represent another indispensable component of the brain connectome and play a significant role in corticocortical connections that support information processing and integration [ 20 ]. In addition, SAFs are the last parts of the brain to myelinate, a process that may extend into the fourth decade of life, increasing the vulnerability of these fibers to damage [ 20 , 21 ]. Previous studies have shown that SAFs are especially vulnerable to the normal aging process [ 22 ] and neuropathological conditions such as Alzheimer’s disease [ 23 ] and schizophrenia [ 24 ]. Owing to the unique characteristics of SAFs, including their small diameter, complex fiber crossings, and distinctive location near the cortex, few studies of SWM have been conducted; existing dMRI techniques have not effectively reconstructed SAFs [ 21 , 25 ]. Recently, advances in dMRI acquisition protocols and tractography processing pipelines have enabled the reliable identification of SAFs in healthy populations and individuals with neurodegenerative diseases [ 21 , 26 ]. Given that structural disconnection represents a key neuropathological feature of ALS [ 14 , 15 ], the microstructural disconnection of SAFs (the key component of the brain connectome) remains largely understudied in this disease [ 19 ]. Thus, the primary aims of this study were as follows: (i) to illustrate the extent and spatial distribution of SAF microscopic morphology impairment in ALS patients via NODDI compared with conventional DTI; (ii) to capture the progressive trend of SAF microstructure damage across advancing ALS clinical stages; (iii) to establish a correlation between SAF impairment and ALS disease severity; and (iv) to assess the efficacy of NODDI parameters related to SAFs as in vivo imaging biomarkers for discriminating ALS from healthy controls (HCs). This study investigated SAF microstructural alterations to elucidate their key role in ALS pathophysiology and identify potential SAF-derived biomarkers for assessing ALS disease severity. Methods Subjects This study was approved by the Research Ethics Committee of Fujian Medical University Union Hospital (No. 2021KJT011) and performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from each participant. This retrospective study included 37 HCs and 87 patients diagnosed with ALS. ALS diagnosis adhered to the revised El Escorial criteria [ 27 ] and Awaji criteria [ 28 ] (see Table 1 ), and disease severity was evaluated via the revised ALS Functional Rating Scale (ALSFRS-R) [ 29 ]. The rate of disease progression was calculated as [(48—ALSFRS-R)/disease duration]. HCs were primarily recruited through local community advertisements to ensure alignment of demographic characteristics with those of the ALS patient group. Table 1. Participant clinical information and demographics HC Stage 1 Stage 2 Stage 3 Stage 4 p value Age (years) 54.4 ± 7.0 55.9 ± 10.0 54.2 ± 10.7 56.1 ± 9.1 56.2 ± 12.3 0.902 a Sex (male/female) 22/15 10/5 24/12 12/13 6/5 0.632 b Education (years) 7.5 ± 3.2 7.1 ± 4.8 7.3 ± 3.8 6.4 ± 4.0 6.8 ± 4.8 0.702 c Site of onset (spinal/bulbar) - 13/2 31/5 22/3 9/2 Diagnosis (possible/probable laboratory-supported/probable/definite) - 7/3/2/3 7/2/21/6 3/1/12/9 3/0/5/3 ASLFRS-R score - 43.9 ± 2.1 41.4 ± 2.6 35.0 ± 6.8 *, # 33.0 ± 6.0 *, # < 0.001 c Disease duration (months) - 14.7 ± 12.0 12.5 ± 7.8 14.4 ± 9.2 19.5 ± 15.1 0.562 c Disease progression rate - 0.5 ± 0.4 0.8 ± 0.5 1.2 ± 0.8 * 1.5 ± 1.8 0.001 c Open in a new tab The numbers are the means ± standard deviations. a, b , and c were determined by one-way analysis of variance, the chi-square test, and the Kruskal‒Wallis test, respectively. The superscript symbols * and # indicate significant differences (Bonferroni-corrected for multiple comparisons) compared with the ALS Stage 1 group and Stage 2 group, respectively. ALSFRS-R , revised amyotrophic lateral sclerosis functional rating scale; HC , healthy control The ALS patients were stratified into four groups according to the King’s scale, which categorizes the spread of motor symptoms across three distinct regions (bulbar, upper limbs, and lower limbs), along with requirements for gastrostomy or noninvasive ventilation [ 2 ]: Stage 1 indicated involvement in the first region ( n = 15); Stage 2 indicated diagnosis or involvement in the second region ( n = 36); Stage 3 indicated involvement in the third region ( n = 25); and Stages 4A/4B indicated needs for gastrostomy or noninvasive ventilation ( n = 11). Table 1 summarizes the clinical and demographic information of the participants. Participants were excluded if they (i) had other neuropsychiatric disorders, such as Alzheimer’s disease, Parkinson’s disease, epilepsy, or major depression; (ii) used psychotropic medications; (iii) had other severe conditions, including respiratory failure, angiocardiopathy, or cancer; and (iv) had contraindications to MRI examination. The summary of participant recruitment and exclusions was shown in Additional file 1: Figure S1. MRI data acquisition We employed a 3 T MRI scanner (Prisma; Siemens Medical Systems, Erlangen, Germany) to procure multimodal images. The diffusion-weighted imaging data were acquired via a sophisticated multishell spin‒echo echo‒planar imaging sequence, incorporating b values of 1000, 2000, and 3000 s/mm 2 , with 30 unique gradient directions for each b value. Additionally, six b value = 0 images were captured. The parameters of interest included repetition time = 4200 ms, echo time = 72 ms, number of averages = 1, slice thickness = 2 mm, field of view = 216 mm × 216 mm, matrix dimensions = 108 × 108, voxel size = 2 mm × 2 mm × 2 mm, flip angle = 90°, and a total of 72 axial slices without interslice gaps. The phase encoding direction was oriented from anterior to posterior, with the multiband factor set to two. The acquisition time for the multishell diffusion-weighted imaging sequence was 7 min and 12 s. Furthermore, we collected T1-weighted (T1W) three-dimensional magnetization-prepared rapid gradient echo sagittal images employing the following parameters: repetition time = 1610 ms; echo time = 2.25 ms; inversion time = 900 ms; field of view = 224 mm × 224 mm; matrix dimensions = 224 × 224; flip angle = 8°; slice thickness = 1 mm; total slice count = 176; and acquisition time = 3 min and 18 s. Data processing The flowchart for MRI data processing was displayed in Fig. 1 . Specifically, we conducted a visual inspection of the T1W and dMRI images for all the participants to identify any signal dropouts or artifacts. We subsequently preprocessed the images via the well-established pipeline outlined below. The preprocessing procedure for both T1W and dMRI data commenced with axial alignment, centering, Gibbs ringing removal according to local subvoxel shifts [ 30 ], and intensity inhomogeneity correction utilizing N4ITK [ 31 ]. Fig. 1. Open in a new tab Flowchart for MRI data processing. The T1-weighted (T1W) and diffusion magnetic resonance imaging (dMRI) data were preprocessed via a well-established pipeline, as outlined in the Data Processing section. Subsequently, diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) techniques were employed to estimate compartment-specific volume fractions from diffusion signals, as detailed in the Biophysical Modeling Section. Whole-brain tractography was then performed to identify short association fibers (SAFs), utilizing the established pipeline described in the SAF Extraction Section. Following the identification of SAFs, quantitative measurements of diffusion were computed after bundle annotation to provide a comprehensive description of the fiber tracts. Finally, statistical analysis was conducted For the dMRI data specifically, we incorporated additional steps: (1) Marchenko–Pastur principal component analysis (MP–PCA) denoising [ 32 ] was applied to increase the signal-to-noise ratio (SNR) without compromising spatial resolution; (2) FSL’s eddy current correction tool was employed for correcting eddy currents [ 33 ]; (3) brain mask generation was performed via a brain extraction tool (BET) [ 33 ]; and (4) distortion correction involved registering individual T1W and dMRI datasets. This transformation was applied to each diffusion-weighted volume, with gradient vectors rotated according to the rotation matrix derived from the affine transformation. To generate brain masks from dMRI data, we utilized a convolutional neural network-based segmentation tool within pnlNipype ( https://github.com/pnlbwh/pnlNipype ). Finally, each individual’s T1W images were transformed from structural space into diffusion space through rigid registration via FSL. Biophysical modeling Various methodologies are employed in biophysical modeling to estimate compartment-specific volume fractions derived from diffusion signals. Diffusion tensors were computed at each voxel with a b value of 1,000 s/mm 2 via a nonlinear tensor operator within the MRtrix3 software package [ 34 ]. By utilizing the estimated diffusion tensor, we subsequently calculated the FA, MD, axial diffusivity, and radial diffusivity. To fit the NODDI model from multishell diffusion MRI data, we utilized the accelerated microstructure imaging via convex optimization (AMICO) toolbox [ 35 ], which enhances both the speed and stability of fitting in regions near the cortex. Additionally, NDI, ODI, and free-water isotropic volume fraction (ISO) maps were generated. Theoretically, the NDI serves as an indicator of neurite density, whereas the ODI quantifies angular variation in neurite orientation, specifically its dispersion and fanning. Extraction of short association fibers To delineate the SAF, we initially performed whole-brain tractography employing a probability fiber tracking algorithm that fits a mixture of fiber orientation distribution functions (fODFs) to the diffusion signal. The fODF was estimated via the multishell multitissue (MSMT)-constrained spherical deconvolution model [ 36 ]. Whole-brain streamlines were generated via the iFOD1 algorithm [ 37 ] with the following parameters: 50 million seeds randomly distributed within gray and white matter volumes, Runge–Kutta fourth-order integration, and angular thresholds of 15° and 30°, constrained by anatomical tissues and cropped at the gray‒white matter interface. The resulting tractograms were filtered via the SIFT algorithm [ 38 ] to mitigate false positive connections [ 39 ]. SAFs represent connections between adjacent gyri located within the cortex or immediately beneath it in the outermost regions of subcortical WM. SAFs can be automatically identified from whole-brain tractography via the steps outlined below [ 40 – 42 ]. First, we warped the fiber streamlines of each subject into Montreal Neurological Institute (MNI) space via affine and nonlinear transformations estimated between the participant's anatomical scan and the atlas. The spatially normalized streamlines from all the participants were then aggregated for population-level fiber clustering using an automated unsupervised fiber clustering method. Second, SAF candidates were determined from whole-brain tractography according to several criteria: (i) the average length of streamlines within a cluster ranges from 20 to 80 mm in common space; (ii) the ratio of streamline length to endpoint distance exceeds 1; (iii) each streamline connects adjacent cortical regions as defined by the Desikan–Killiany atlas; (iv) no streamline connects both hemispheres; and (v) no streamline involves specific regions such as the corpus callosum, thalamus, hippocampus, amygdala, caudate nucleus, putamen, cerebellum, or brainstem. Third, we utilized TractDL [ 42 ] to partition SAF candidates into distinct fiber clusters, resulting in a total of 187 valid SAF bundles that were automatically identified with high consistency and named according to their connected cortical regions. These 187 fiber clusters exhibited remarkable consistency across all the participants: 185 out of 187 clusters (98.93%) were detected in every participant, whereas all 187 clusters were observed in 111 participants (98.23%). The spatial distribution of the extracted SAFs was visually presented in the Additional file 1: Video S1. Statistical analysis Diffusion measurements were computed following the annotation of the fiber bundles to provide a quantitative description of the fiber tracts. For each SAF bundle, we calculated the mean statistic for all points along the fibers for each measurement. The general linear model was used to compare intergroup differences in dMRI metrics for each SAF bundle, adopting age, sex, and years of education as the covariates. For each diffusion metric, a permutation t -test (5000 permutations) was performed to determine differences for each of the 187 SAF bundles. Multiple comparison correction across all 187 tract-specific tests per metric was conducted via the Benjamini–Hochberg false discovery rate (FDR) approach, with significance set at q < 0.05. For diffusion measurements that exhibited significant intergroup differences, we investigated their correlation with clinical parameters (including the ALSFRS-R score, disease duration, and rate of disease progression) using partial correlation analysis, while controlling for potential confounders including age, sex, and education level. Additionally, we evaluated their differential potential between ALS patients and HCs, through receiver operating characteristic (ROC) curve analysis. To evaluate the combined discriminated performance of NDI and ISO, a binary logistic regression model was constructed. The predicted probabilities derived from this model were used as a composite index for ROC curve analysis, and the area under the ROC curve (AUC) was computed to assess the joint classification accuracy. Results The four cohorts of patients and healthy controls were matched in terms of age, sex, and years of education (Table 1 ). The ASLFRS-R scores gradually decreased with increasing clinical stage of ALS ( p < 0.001), whereas the rate of disease progression increased across these stages ( p = 0.001). Notably, there was no significant difference in disease duration among the four groups of ALS patients ( p = 0.562). Figure 2 illustrates the whole-brain SAF mapping and the fibers exhibiting intergroup differences in NODDI measurements. Among the entire brain’s SAFs, seven fibers connecting the left postcentral-precentral gyrus, left precentral-precentral gyrus, right postcentral-precentral gyrus, right paracentral-posterior cingulate gyrus, left paracentral-posterior cingulate gyrus, left precentral-superior parietal gyrus, and left precentral-superior frontal gyrus demonstrated significant NDI differences across the four groups of patients and HCs. Additionally, one fiber connecting the left medial orbitofrontal-rostral anterior cingulate gyrus exhibited an ISO difference ( p < 0.05 with FDR correction) (Fig. 3 ). In these identified fibers, a trend toward progressive NDI reduction alongside an increase in ISO was observed as the ALS stage advanced compared with that in HCs (Fig. 4 and Table 2 ). No intergroup differences were detected in the ODI or DTI parameters. Fig. 2. Open in a new tab Whole-brain mapping of SAFs and fibers with differences in NODDI measurements. The fibers are shown from the right (R), left (L), anterior (A), and superior (S) perspectives. The red, blue, and green colors represent fiber bundle orientations running left–right, up-down, and forward–backward, respectively. Significant fibers are reported at a p value < 0.05 with false discovery rate (FDR) correction applied. SAF, short association fiber; PoCG-PrCG, postcentral-precentral gyrus; PrCG-PrCG, precentral-precentral gyrus; PaCG-PCG, paracentral-posterior cingulate gyrus; PrCG-SPG, precentral-superior parietal gyrus; PrCG-SFG, precentral-superior frontal gyrus; MOFG-RACG, medial orbitofrontal-rostral anterior cingulate gyrus; NDI, neurite density index; ISO, isotropic volume fraction Fig. 3. Open in a new tab Spatial distribution of each short association fiber, highlighting differences in the NDI and ISO (first four columns), alongside an illustration of the corresponding cortical regions connected by these fibers (last column). The fibers are presented from the right (R), left (L), anterior (A), and superior (S) perspectives. The red, blue, and green colors represent the orientations of the fiber bundles extending left–right, up-down, and forward–backward, respectively. L, left; R, right; PoCG-PrCG, postcentral-precentral gyrus; PrCG-PrCG, precentral-precentral gyrus; PaCG-PCG, paracentral-posterior cingulate gyrus; PrCG-SPG, precentral-superior parietal gyrus; PrCG-SFG, precentral-superior frontal gyrus; MOFG-RACG, medial orbitofrontal-rostral anterior cingulate gyrus; NDI, neurite density index; ISO, isotropic volume fraction Fig. 4. Open in a new tab Group differences in NDI and ISO scores between healthy controls and patients with amyotrophic lateral sclerosis across the four clinical stages. The distributions of the NDI and ISO are illustrated; a shift of the distribution curve toward negative values indicates a greater decrease in the NDI, whereas a shift toward positive values signifies an increase in the ISO. Stars marked with distinct colors denote significant contrasts (corrected p < 0.05). PoCG-PrCG, postcentral–precentral gyrus; PrCG-PrCG, precentral–precentral gyrus; PaCG-PCG, paracentral–posterior cingulate gyrus; PrCG-SPG, precentral–superior parietal gyrus; PrCG-SFG, precentral-superior frontal gyrus; MOFG-RACG, medial orbitofrontal-rostral anterior cingulate gyrus; NDI, neurite density index; ISO, isotropic volume fraction Table 2. Intergroup differences in NODDI metrics in SAFs HC Stage 1 Stage 2 Stage 3 Stage 4 p corrected NDI measure L PoCG-PrCG fiber 0.494 ± 0.027 0.489 ± 0.026 0.473 ± 0.031 0.466 ± 0.033 0.464 ± 0.036 0.005 L PrCG-PrCG fiber 0.530 ± 0.028 0.520 ± 0.033 0.510 ± 0.037 0.510 ± 0.038 0.481 ± 0.033 0.005 R PoCG-PrCG fiber 0.530 ± 0.039 0.528 ± 0.045 0.518 ± 0.044 0.516 ± 0.039 0.464 ± 0.058 0.020 R PaCG-PCG fiber 0.558 ± 0.030 0.559 ± 0.034 0.541 ± 0.038 0.539 ± 0.032 0.518 ± 0.037 0.008 L PaCG-PCG fiber 0.557 ± 0.036 0.564 ± 0.033 0.550 ± 0.037 0.530 ± 0.042 0.524 ± 0.042 0.022 L PrCG-SPG fiber 0.526 ± 0.029 0.506 ± 0.031 0.505 ± 0.037 0.501 ± 0.033 0.488 ± 0.030 0.008 L PrCG-SFG fiber 0.566 ± 0.026 0.567 ± 0.020 0.552 ± 0.035 0.557 ± 0.030 0.520 ± 0.050 0.020 ISO measure L MOFG-RACG fiber 0.018 ± 0.009 0.021 ± 0.008 0.023 ± 0.011 0.026 ± 0.013 0.027 ± 0.010 0.040 Open in a new tab The data are presented as the means ± standard deviations. The p values corrected by the false discovery rate (FDR) method are shown. HC , healthy control; ISO , isotropic volume fraction; NDI , neurite density index; NODDI , neurite orientation dispersion and density imaging; L , left; R , right; PoCG-PrCG , postcentral-precentral gyrus; PrCG-PrCG , precentral-precentral gyrus; PaCG-PCG , paracentral-posterior cingulate gyrus; PrCG-SPG , precentral-superior parietal gyrus; PrCG-SFG , precentral-superior frontal gyrus; MOFG-RACG , medial orbitofrontal-rostral anterior cingulate gyrus; SAFs , short association fibers Figure 5 illustrates the relationship between NODDI measurements and clinical parameters in ALS patients. Following FDR correction, a significant positive correlation was identified between the NDI in the seven SAFs and the ALSFRS-R score. Fig. 5. Open in a new tab Correlations between NDI scores and ALSFRS-R scores among patients diagnosed with amyotrophic lateral sclerosis. NDI, neurite density index; ALSFRS-R, revised amyotrophic lateral sclerosis functional rating scale; PoCG-PrCG, postcentral-precentral gyrus; PrCG-PrCG, precentral-precentral gyrus; PaCG-PCG, paracentral-posterior cingulate gyrus; PrCG-SPG, precentral-superior parietal gyrus; PrCG-SFG, precentral-superior frontal gyrus. A significant correlation was reported at a P value < 0.05 with false discovery rate (FDR) correction Table 3 presents the findings of the ROC analysis utilizing either the NDI or ISO index for each SAF. By integrating these NDI and ISO metrics, a moderate classification potential was observed (AUC = 0.780, p < 0.001), as depicted in Fig. 6 . Table 3. Area under the receiver operating characteristic curve obtained in the analyses using NDI and ISO measures in SAFs Measures SAF Area under curve 95% Confidence Interval p value NDI L PoCG-PrCG fiber 0.693 0.593–0.794 0.001 NDI L PrCG-PrCG fiber 0.669 0.574–0.764 0.003 NDI R PoCG-PrCG fiber 0.621 0.516–0.726 0.033 NDI R PaCG-PCG fiber 0.643 0.541–0.745 0.012 NDI L PaCG-PCG fiber 0.621 0.513–0.73 0.033 NDI L PrCG-SPG fiber 0.709 0.613–0.806 < 0.001 NDI L PrCG-SFG fiber 0.595 0.492–0.697 0.096 ISO L MOFG-RACG fiber 0.706 0.599–0.813 < 0.001 Combining NDI and ISO Above 8 fibers 0.780 0.699–0.861 < 0.001 Open in a new tab ISO , isotropic volume fraction; NDI , neurite density index; L , left; R , right; PoCG-PrCG , postcentral-precentral gyrus; PrCG-PrCG , precentral-precentral gyrus; PaCG-PCG , paracentral-posterior cingulate gyrus; PrCG-SPG , precentral-superior parietal gyrus; PrCG-SFG , precentral-superior frontal gyrus; MOFG-RACG , medial orbitofrontal-rostral anterior cingulate gyrus; SAFs , short association fibers Fig. 6. Open in a new tab The NODDI measure (combined both NDI and ISO metrics) within SAFs revealed moderate classification efficacy (AUC = 0.780, p < 0.001) Discussion In this study, we assessed the microstructural impairment pattern of SAFs across different clinical stages of ALS via the NODDI and established its correlation with disease severity. The key findings were as follows: (i) ALS is distinguished by a reduction in the NDI and an increase in the ISO within SAFs. (ii) Damage to these fibers affects both motor-related and extramotor regions in individuals with ALS. (iii) Microstructural impairment of the SAFs deteriorates concomitantly with advancing clinical staging and motor disability in ALS patients. (iv) NODDI offers quantitative biomarkers associated with SAFs for evaluating disease severity and staging in ALS. (v) NODDI outperforms conventional DTI in detecting SAF microstructural abnormalities in ALS. These findings demonstrate that the impairment of the brain SAFs across clinical stages could serve as a new biomarker of disease severity in ALS, highlighting the utility of higher-order diffusion MRI to elucidate the pathophysiological underpinnings of ALS and stratify patients with ALS. In the present study, lower NDIs were observed in several SAFs, potentially reflecting multiple pathological processes occurring in ALS. Previous DWM-related studies have generally related a decreased NDI to axonal disruption and demyelination in ALS patients [ 9 ]; moreover, MRI-histopathological correlation studies have indicated a decrease in axonal density and unraveling of myelin sheaths in SAFs in ALS patients [ 10 , 43 ]. Therefore, the reduced SAF NDI likely reflects both axonal loss and myelin degradation in ALS patients. Furthermore, SAFs exhibit a greater proportion of dendritic components than DWMs do [ 44 ]. Dendritic degeneration, another common pathological feature in ALS [ 45 ], may also partially account for the observed NDI reduction. In addition to a reduced NDI, increased ISO values were observed in ALS patient SAFs. Although few MRI-pathological correlation studies have directly examined SAFs in individuals with ALS, histological analyses of DWM have generally linked elevated ISO values to myelin loss or neuroinflammation [ 46 ]. These findings suggest that the ISO elevation in SAFs may also reflect similar pathological changes. Contrary to our expectations, this study revealed no significant changes in the ODI in the SAFs of ALS patients. This negative finding aligns with previous reports demonstrating that ODI alterations are not a prominent neuropathological feature of ALS [ 47 , 48 ]. Our study revealed microstructural abnormalities in the SAFs of ALS patients, characterized by a reduced NDI in the identified fibers connecting the left postcentral–precentral gyrus, left precentral–precentral gyrus, right postcentral–precentral gyrus, and left precentral–superior parietal gyrus. These findings suggest that microstructural damage to WM fiber bundles in ALS patients is not confined to motor-related brain regions but also extends to somatosensory-related brain regions. Notably, these aberrant fiber bundles link key brain regions within the SMN [ 49 ]. Consistent with our findings, a previous network-based statistic (NBS) study demonstrated a significant reduction in structural connectivity within the SMN of ALS patients, primarily affecting bilateral and intrahemispheric connections between sensorimotor nodes [ 50 ]. The SMN is responsible for integrating somatosensory feedback within motor planning and execution, and damage to the microstructure of the SAF that connects the brain regions comprising the SMN may indicate impaired communication and coordination within this network [ 49 ]. Impaired SAF microstructure within the SMN in ALS patients, as found in our study, provides structural evidence for SMN functional damage in ALS patients [ 49 ]. Furthermore, correlation analysis revealed that NDI in SAFs connecting the brain regions comprising the SMN is associated with disease severity in ALS patients, suggesting that structural disconnection within the SMN may serve as one of the key mechanisms contributing to motor deficits in ALS patients. This study further demonstrated that SAFs in ALS patients, which exhibit microstructural damage, connect brain regions associated with sensorimotor and higher-order cognitive function. Specifically, these fiber bundles include (i) the SAF connecting the bilateral paracentral-posterior cingulate gyrus, which serves as the structural pathway between the SMN and default mode network (DMN), and (ii) the SAF linking the left precentral-superior frontal gyrus, which acts as the structural pathway between the SMN and the cognitive control network (CCN). Previous NBS analysis also revealed that, in ALS patients, the structural connectivity between the paracentral gyrus and posterior cingulate gyrus is significantly decreased [ 51 ]; moreover, the structural connectivity between the precentral gyrus and superior frontal gyrus is significantly reduced in ALS patients [ 50 ]. These results are in line with our study findings. Previous studies have shown that enhanced functional connectivity between the SMN and DMN is beneficial for the recovery of motor function after stroke [ 52 ], and enhanced functional connectivity between the SMN and CCN is conducive to the recovery of voluntary action and speech disturbance in patients with supplementary motor area syndrome [ 53 ], which suggests that the functional coupling of the SMN with the DMN and CCN is closely related to motor performance. On the basis of these findings, structural disconnection between the SMN and the DMN/CCN may represent one of the potential mechanisms contributing to motor dysfunction in individuals with ALS. Notably, our study also revealed microstructural damage in the SAF connecting the left medial orbitofrontal and rostral anterior cingulate gyrus, which play critical roles in emotion processing, decision-making, and social behavior [ 54 ]. In addition to motor deficits, ALS patients exhibit cognitive and behavioral alterations involving difficulty with decision-making, apathy, disinhibition, and loss of sympathy and empathy [ 55 ]. Microstructural damage to the SAF connecting the left medial orbitofrontal and rostral anterior cingulate gyrus may underlie the mechanisms involved in cognitive and behavioral impairments in patients with ALS. In alignment with previous studies [ 12 , 56 ], our results also demonstrated that the NODDI parameters outperformed conventional DTI metrics in detecting SAF microstructural changes. This detected advantage stems from NODDI’s ability to separately quantify intra-axonal degeneration (reflected by an NDI decrease) and extracellular pathology (reflected by an ISO increase), providing more specific biological insights than DTI’s composite measures [ 10 ]. Moreover, NODDI-derived metrics from the SAF demonstrated progressive alterations with increasing disease stage in ALS patients and showed significant correlations with clinical disease severity, highlighting their potential as quantitative biomarkers. Several limitations warrant acknowledgment. First, the sample size of this study was relatively modest. This was partly attributable to the limited tolerance for MRI scanning among patients with advanced ALS, as well as the potential for misdiagnosis in very early-stage cases, resulting in fewer participants at both early and late disease stage. This constraint may curtail statistical power, thereby potentially diminishing the sensitivity of NODDI in discerning intergroup SAF microstructural differences and limiting the generalizability of the findings to the broader ALS population. Future studies with larger and multicenter cohorts are warranted to validate and extend these findings. Second, follow-up MRI scans and clinical evaluations were not performed; thus, the causal relationships between SAF structural damage and ALS progression alongside motor disability necessitate further elucidation through future longitudinal studies. Third, subsequent investigations should employ diffusion data acquired from MRI scanners equipped with high-performance gradients (e.g., up to 300 mT/m) during tractography. Enhanced spatial resolution is crucial for the accurate mapping of SAFs because of their brief lengths and positioning within SWM regions where partial volume effects and intricate fiber crossings must be meticulously addressed [ 57 ]. Fourth, a simultaneous assessment of SAFs in SWM and larger fiber bundles in DWM should be undertaken; such an approach would facilitate a more comprehensive understanding of WM damage patterns associated with ALS severity. Fifth, this study did not assess the cognitive function of ALS patients and perform genetic stratification analysis, which may restrict the understanding of disease phenotyping [ 58 , 59 ]. Sixth, to verify the discriminatory value of SAF-derived biomarkers for distinct motor neuron diseases, future studies should simultaneously include other subtypes of motor neuron diseases (such as primary lateral sclerosis and progressive muscular atrophy) or ALS-mimic syndromes to support differential diagnostic analyses. Conclusions In summary, both intra- and inter-gyral microstructural impairments within SAFs, primarily manifested as neurite injury affecting both motor and extramotor areas, were observed in individuals with ALS, which exacerbated over the course of the disease. This finding underscores that SAF microstructural damage constitutes a pivotal element of ALS pathophysiology and serves as an additional biomarker reflective of disease severity. Furthermore, NODDI offers superior quantitative biomarkers compared with conventional DTI by capturing SAF microstructural abnormalities more effectively in patients with ALS. Supplementary Information 12916_2026_4770_MOESM1_ESM.zip (185MB, zip) Supplementary Material 1. Supplementary Materials provided by the authors for additional information about their work. It contains Figure S1 and Video S1. Figure S1. Flowchart for participant recruitment and exclusions. (A) The process of enrolling and excluding patients with amyotrophic lateral sclerosis; (B) The process of enrolling and excluding healthy controls. The assessment of the participants’ family and individual disease history and the clinical examination were conducted by experienced neurologists and other trained researchers at our center. Video S1. The spatial distribution of the extracted short association fibers (SAFs). Acknowledgements Not applicable. Abbreviations ALS Amyotrophic lateral sclerosis ALSFRS-R Revised ALS Functional Rating Scale CCN Cognitive control network DMN Default mode network dMRI Diffusion magnetic resonance imaging DKI Diffusion kurtosis imaging DTI Diffusion tensor imaging DWM Deep white matter FA Fractional anisotropy ISO Isotropic volume fraction MD Mean diffusivity NBS Network-based statistic NDI Neurite density index NODDI Neurite orientation dispersion and density imaging ODI Orientation dispersion index SAF Short association fiber SMN Sensorimotor network SWM Superficial white matter Authors’ contributions NXH: Data curation, Formal analysis, Funding acquisition, Investigation, and Writing – original draft. ZWC: Data curation, Investigation, and Writing – original draft. SPZ: Data curation, Formal analysis, Investigation, and Writing – original draft. HYL: Data curation and Investigation. SC: Conceptualization, Data curation, and Formal analysis. ZYZ: Conceptualization, Data curation, Formal analysis, Supervision, Writing – review and editing. YW: Methodology, Software, Validation, and Visualization. HJC: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision, Writing – review and editing. All authors read and approved the final manuscript. Funding This study was supported by grants from the National Natural Science Foundation of China (No. 82572160), Fujian Provincial Health Technology Project (No. 2023CXA009), Natural Science Foundation of Fujian Province (No. 2024J01625), and Fujian Province Joint Funds for the Innovation of Science and Technology (Nos. 2024Y9253 and 2024Y9256). Data availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of Fujian Medical University Union Hospital (No. 2021KJT011) and performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from each participant. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Nao-Xin Huang, Zi-Wei Cai and Shao-Peng Zhuang are equal contributors. Contributor Information Sheng Chen, Email: [email protected]. Zhang-Yu Zou, Email: [email protected]. Ye Wu, Email: [email protected]. Hua-Jun Chen, Email: [email protected]. References 1. Goutman SA, Hardiman O, Al-Chalabi A, Chió A, Savelieff MG, Kiernan MC, et al. 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It contains Figure S1 and Video S1. Figure S1. Flowchart for participant recruitment and exclusions. (A) The process of enrolling and excluding patients with amyotrophic lateral sclerosis; (B) The process of enrolling and excluding healthy controls. The assessment of the participants’ family and individual disease history and the clinical examination were conducted by experienced neurologists and other trained researchers at our center. Video S1. The spatial distribution of the extracted short association fibers (SAFs). Data Availability Statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. 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