Characterization of brain microstructural abnormalities in spasmodic dysphonia: A preliminary diffusion kurtosis imaging study

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DKI (b values = 0, 1,000, and 2,000 s/mm 2 ) was performed for 20 SD patients and 20 controls. DKI parameters including kurtosis fractional anisotropy (FA), mean diffusion (MD), and mean kurtosis (MK). Python3.11 and MATLAB R2018b with tract-based spatial statistics were used to compare group differences. The correlation analysis was used to assess the correlation between changes in FA and clinical measures. Compared to healthy controls, SD patients showed significantly reduced FA and increased MD in the white matter (WM) and gray matter (GM). Increases of MK had broader distributions than MD. In the WM, the FA and MD of frontal and jitter values had significant inverse correlations in SD patients. The FA and MD were negative correlated with grade, and the FA in the frontal region had a negative correlation with asthenia. Patients with SD exhibited microstructural changes in brain regions in charge of motor conduction and auditory functions. The WM diffusion metric changes had negative correlations with clinical symptoms of SD. The brain changes could modify the central control of voluntary vocalizations and may consequently be the pathophysiologic reason for SD. Health sciences/Neurology/Neurological disorders/White matter disease Health sciences/Neurology/Neurological disorders Spasmodic dysphonia Magnetic Resonance Imaging Diffusion Kurtosis Imaging Neuroimaging Voice Assessment Figures Figure 1 Figure 2 Introduction Spasmodic dysphonia (SD) is the laryngeal form of focal dystonia which is characterized by involuntary spasms of the laryngeal muscles in speech 1 . And it is also a task-specific disability, affecting voice production only during speech, but not during vocal expressions of emotions 2 . SD has a low prevalence and predominately affects females 3 . SD presents with different clinical manifestations, with the most prevalent subtypes being adductor and abductor SD phenotypes, which are characterized by involuntary spasms in the laryngeal muscles during speech 4 . The precise etiology and pathophysiology of SD is still unclear. Roberts' study provided the first clinical evidence that SD is a speech disorder resulting from a neurological dysfunction, or possibly a structural neurological disorder 5 .The histopathologic and magnetic resonance imaging (MRI) findings of recurrent laryngeal nerve demyelination and infarcts or demyelinating lesions of the brain further indicated that SD is a central nervous system disorder 6 , 7 . A number of studies 8 – 11 , using various functional brain imaging techniques, have provided compelling evidence that the disorder is associated with structural and abnormal activities involving the speech sensorimotor network. Voxel-based morphometry revealed that patients with SD have abnormalities in critical structures of the speech control system, including the laryngeal sensorimotor cortex, and in structures commonly abnormal in other primary dystonia, with increased gray matter (GM) volume and cortical thickness 11 . Furthermore, functional neuroimaging studies of SD have identified abnormal activities (increases/decreases) in central pathways required for control of learned voice production 11 – 13 . For example, functional neuroimaging studies of SD patients have reported hyperactivation in the auditory-related areas and motor speech areas. The left primary sensory area and bilateral subcortical nucleus had lower activation 14 . The presence of functional abnormalities suggests that brain structures may be altered in SD patients. Carbon et al 15 used diffusion tensor imaging (DTI) and reported that fractional anisotropy (FA) was reduced in the sub-gyral white matter (WM) of the sensorimotor cortex of DYT1 carriers, which may contribute to the susceptibility of DYT1 carriers to develop clinical manifestations of dystonia. DTI demonstrated that the FA was decreased in the right genu of the internal capsule, and the overall water diffusivity was increased in the bilateral corticobulbar/corticospinal tract in SD patients 16 . Furthermore, water diffusivity was increased in cerebellar white matter and GM. It was also revealed that diffusivity changes were associated with the clinical manifestations of SD. In conclusion, previous studies have indicated that brain abnormalities may modify the central control of voluntary voice production and could contribute to the pathophysiology of SD. However, the above results indicated inconsistencies in directional diffusivities, which may be attributed to small cohort sizes or inherent limitations of the DTI model. DTI utilizes free water diffusion, also known as Gaussian diffusion, to provide in vivo quantitative assessments of microstructural integrity and organization 17 . Owing to the barriers such as organelles and cell membranes, however, water molecules tend to exhibit non-Gaussian diffusion. Thus, the sensitivity of DTI in evaluating microstructural integrity is probably not entirely optimal. Diffusional kurtosis imaging (DKI) can quantify non-Gaussian diffusion characteristics, with image acquisition involving higher b-values (≥ 2,000 s/mm 2 ), as well as more gradient orientations directions, and may involve more exact and specific parameters to characterize the brain microstructure 17 , particularly in brain regions of high tissue heterogeneity such as GM 18 , 19 . Furthermore, DKI demonstrates greater sensitivity and specificity in evaluating developmental and pathological alterations in neural tissues, and consequently may be a useful and noninvasive indicator of brain tissues affected by various diseases 20 – 22 . The purpose of our study was to characterize brain tissue microstructural damage and to estimate the performance of the DKI in detecting GM and WM abnormalities in SD patients. We concurrently analyzed the correlation between DKI parameters and clinical examination in SD patients to evaluate the potential of these metrics as supplementary indicators of intracranial disease severity. Materials and methods Study participants Participants included 20 patients with SD (all females; mean age = 44.41 years; range, 23–68 years) and 20 healthy volunteers (all females; mean age = 42.50 years; range, 24–66 years) serving as controls. All 40 subjects were strictly right-handed. Establishment of a definitive SD diagnosis based on the criteria of Ludlow et al 23 . SD was diagnosed by otolaryngologic examinations and speech/language assessment. Inclusion criteria were: (1) no developmental variants in the larynx; (2)no botulinum toxin injections for SD or poor improvement after voice therapy; (3) no previous treatment with neuroleptic drugs. None of participants had a past or present history of any neurological (except SD), psychiatric or laryngeal disorders, intracranial vascular malformations, participants with normal MRI scans were recruited from among the spouses of patients. Evaluation Acoustic analysis was conducted in a standard quiet voice testing room using a XION DiVAS v2.3 and a head wearing microphone. A long /a/ vowel was recorded in a tone by adjusting the distance between the patient's mouth and the microphone to 30 cm. The parameters of the fundamental frequency (F0), jitter (%) and shimmer (%) were recorded. The subjective evaluations included auditory perception grading(GRBAS) and two of patient self-assessment tools Voice Handcap Index(VHI)and Voice-Related Quality of Life Scale (V-RQOL). GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) is a five-dimensional scale auditory perception assessment system, which was scored on a 4-point scale 24 . Voice Handicap Index (VHI) is a valid tool which is commonly used scale to measure voice handicap, with three information on the functional, emotional and physical attributes of the voice disorder. The participants scored five points for each question 25 . V-RQOL assesses each participant’s voice-related quality of life, which comprises ten items in a physical functioning and social-emotional 26 . The patients underwent MR examination using 3T MR (Discovery MR750, GE Healthcare, Waukesha, WI, USA) with 8-channel head phased-array coils. High-resolution anatomical images were obtained with axial T1-weighted three-dimensional brain volume imaging sequences. The sequence parameters are shown in Table 1. b values = 0, 1000, and 2000 s/mm 2 ; 30 diffusion encoding directions for each nonzero b value; and scan time 10 min and 35 s. Table 1 MRI protocol. Sequences TR (ms) TE (ms) NEX Slice Thickness (mm) FOV (mm) Matrix T 2 WI 6372 100 1 6 240 × 240 352 × 352 T 1 WI 8.2 3.2 1 1.2 240 × 240 256 × 256 DWI 4500 98 1 4 240 × 240 128 × 128 Label: SE = spin echo, FSE = fast spin echo, TR = repetition time, TE = echo time, NEX = number of excitations, FOV = field of view, DWI = diffusion-weighted imaging. Image processing and analysis DKI parameters (FA, MD, and MK) were measured and processed using python3.11 (https://www.python.org) and MATLAB R2018b (https://www.mathworks.com/products/matlab.html). DKI data were first preprocessed by denoising. The DKI data were first stripped for each participant. Tract-based spatial statistics (TBSS) were implemented using the Dipy (https://dipy.org/) to alleviate partial volume effects. We used the skeleton projection step of TBSS to analyze the WM and GM. First, we aligned all FA images to the standard AAL_61x73x61_YCG.nii. T1-weighted images were registered to AAL standard-space images to provide high resolution, anatomical references for the automatic delineation of regions of interest. Then, other images in the standard space were averaged, and the mean FA skeleton was used as a representation of the centers of all tracts common to the groups. Finally, each participant's images were projected onto the mean skeleton, and the MK and MD images were presented onto the mean FA skeleton. MATLAB R2018b was used to extract FA, MK, and MD values. Statistical Analysis Statistical analyses were conducted with SPSS Statistics 26.0 software (IBM, Armonk, NY, USA). Continuous variables are described as mean ± standard deviation. The Student's t-test for normally distributed continuous variables, the Mann–Whitney test for non-normally distributed continuous variables, and the chi-square test or the Fisher’s exact test for categorical variables. Differences with a corrected p-value of less than 0.05 were considered significant. Non-parametric Spearman or parametric Pearson correlation coefficient are used to identify correlations between the DKI parameters. Both Spearman and Pearson correlation coefficients are interpreted according to Cohen et al 27 : (1) 0.10–0.29 represents a weak correlation; (2) 0.30–0.49 represents a moderate correlation; and (3) > 0.50 represents a strong correlation. Ethics approval and consent to participate. This study was approved by the institutional review board (No.HZKY-PJ-2023-32). All participants provided written informed consent according to the guidelines of the Ethics Committee. The methods in the current study were performed in accordance with the relevant guidelines and regulations. Results Clinical factors The SD group comprised 20 subjects (all females; mean age=44.41 years; range, 23–68 years). The control group consisted of 20 subjects (all females; mean age=42.50 years; range, 24–66 years). The baseline clinical factors, acoustic voice analysis measurements, and auditory-perceptual analysis findings of SD patients and control subjects are reported in table 2. All controls were sex-matched to patients. There was no significant difference between the patients and controls according to age ( Z =–0.108, P=0.914). Kurtosis parameters from the DKI Figure 1 shows the parametric maps of FA, MK, and MD from SD subjects. The results indicated a significant decrease in FA in patients with SD, when compared with controls (P<0.05). Decreases in FA in SD patients were observed in the GM of the left inferior temporal gyrus, anterior and posterior cingulate cortices, caudate nucleus, right inferior frontal operculum, as well as in the WM of the left precentral gyrus, superior frontal gyrus, supplementary motor area (SMA), fusiform gyrus, angular gyrus, posterior cingulate gyrus, middle temporal gyrus, and the right superior temporal pole and both anterior cingulate gyrus, inferior frontal operculum gyrus, caudate nucleus, thalamus, and inferior temporal gyrus (Figure 2). Compared with control subjects, SD patients had significantly increased MD (P<0.05) and MK in multiple GM and WM regions. The MD was increased in SD located in the WM within the left inferior occipital gyrus, superior frontal orbital gyrus, inferior temporal gyrus, fusiform gyrus, and the right lingual gyrus. The MK were increased in SD located in the GM within the left lingual gyrus and the right inferior frontal operculum gyrus, inferior frontal orbital gyrus, rolandic operculum, superior frontal orbital gyrus, middle frontal orbital gyrus, medial frontal orbital gyrus, rectus gyrus, and in the WM in the left thalamus, superior temporal gyrus, anterior cingulate gyrus, and the right precentral gyrus, superior frontal gyrus, inferior frontal operculum gyrus, superior medial frontal gyrus, and putamen. Correlations between diffusion parameters and clinical scores In the WM, the FA and MD of Frontal_Sup_L and jitter values, the MD of Frontal_Sup_Orb_L and jitter values had significant inverse correlations, which were greater in SD patients (r =–0.565, P=0.009; r =–0.460, P=0.041, respectively) than in the control group. In the WM, the FA and MD in regions that differed between the two groups (FA in the Cingulum_Ant_L and MD in the Temporal_Inf_L) were also examined for relationships with numbers of G in SD patients. We found negative correlations between the FA, MD, and G ( r =–0.474, P=0.035; r =–0619, P=0.004, respectively) in SD patients; and the FA in Frontal_Sup_L also had negative correlations with A (r =–0.627, P=0.003). Discussion Our study made significant observations regarding WM and GM microstructural changes in SD patients using DKI. The analysis revealed that SD is associated with disruptions in the normal architecture of WM and GM. In comparison to controls, SD patients had abnormalities, reflected by decreased FA, and increased MD and MK, in several WM and GM areas, especially within the frontal, temporal, caudate nucleus, and SMA. Our findings are consistent with neuroimaging studies showing that SD consistently showed abnormalities in the sensorimotor cortex, basal ganglia, thalamus, WM, and GM connections 28,29 . Additionally, we identified correlations between changes in WM diffusion metrics and the severity of SD symptoms. Therefore, the brain abnormalities identified in this study can be considered primary alterations underlying the pathophysiology of SD. Widespread WM differences were found in SD patients as indicated by the FA, which was consistent with the results of previous studies 30,31 . DTI findings and regional microstructural abnormalities were found in the thalamus, caudate nucleus, cingulum bundle, and temporal lobe 16,29 . Our study provides a thorough outline of WM changes in SD patients through DKI parameters derived using a global analysis approach. FA gauges the extent of anisotropy of water molecules and indicates preferential movement of water molecules in one direction. The water molecules diffuse through neural fibers in the brain, particularly via axonal tracts, so the FA is considered an indirect measure of axonal integrity 32,33 . The regions where FA were reduced in SD patients represented positions where the molecular movement within axonal fibers was not as structured and homogeneous as in control subjects, therefore corresponding to changes in WM integrity and axonal impairment 18 . While the physiological correlates of abnormal FA remain speculative, these brain alterations have been confirmed through focal histopathological analyses, which identified reduced WM density due to decreased myelin content and scattered microglial/macrophage activation in SD patients 16 . The idiosyncratic character of the brain microstructural abnormalities substantiated their confinement to the corticobulbar/corticospinal tract and their primary input/output structures, suggesting an association with SD. It should be noted that in our results of SD, FA in the left supplementary motor area (SMA) was reduced. SMA is situated in Brodmann’s area 6 of the frontal lobe, and plays an important role in motor initiation, while the pre-SMA has a significant role in behavioral energization and inhibition 34 . According to an MRI study of Pool et al, a majority of SD patients have rapid alternating motion (RAM) and abnormal limb rhythms. As RAM impairment might involve the basal ganglia or upper motor neuron pathways, it has been demonstrated that involvement of a pallidothalamic-SMA system could be present in SD patients 35 . Moreover, MRI findings from a patient with adductor-type SD showed that the SMA was not activated during the phonation task 12 . The above findings suggest a defective SMA function in SD patients, which ought to be intimately associated with the generation of pathogenic voice. Reduced FA values in the SMA region in SD patients suggest the presence of microstructural abnormalities. In comparison to DTI, DKI has been effective for monitoring microstructural changes by using additional kurtosis metrics 36 . MD values represent the apparent diffusion coefficient corrected for non-Gaussian distribution 36 . MD is also considered to be an indirect measure of axonal integrity 32,33 . The alteration of MD is probably associated with the decreased cellularity. And the diffusion variations may be related to changes in the space available for extracellular water. As a result, the increased MD is hypothesized to be associated with a larger extracellular space. Our results showed increased MD in SD, particularly in the WM of the left inferior occipital gyrus, superior frontal orbital gyrus, inferior temporal gyrus, and fusiform gyrus, consistent with findings by Bonilha et al 37 . They reported that idiopathic dystonia was related to the increase in MD in adjacent WM, bilateral to the pallidum and putamen, and in subcortical hemispheric regions. The changes of MD may be related with cellular membrane breakdown, cell death, and tissue cavitation 38 . In the present study, changes of MK had a broader distribution than that of MD. MK is the average value of kurtosis over all possible gradient directions, which can indicates variations in structural complication 39,40 . Previous studies have reported that MK reflected neuronal shrinkage 40 , changes in axonal and myelin density 41 , and astrogliosis 42 . An animal study also reported that MK closely linked to neurite density in the caudate putamen 43 . Thus, our study further indicated that DKI was more sensitive to alterations in regions of WM microstructure in SD patients. WM involves the linkages between encephalic areas, and is accountable for information exchange and communication between GM. WM abnormalities are recognized as the consequence of Wallerian degeneration, which is secondary to the loss of neurons in the GM 44-46 . We observed lower FA in the frontal lobe and higher MK values in the temporal lobe of patients with SD. FA and MK represent the overall microstructure complexity of brain tissue. Therefore, changes in FA and MK represent alterations in GM microstructure, which may result from neurodegeneration, involving decreased dendrite spines and cell densities 18,47 . Such variations will not automatically induce observable volumetric variation in structural imaging. While several studies identified the patients with SD have decreased cortical areas in the bilateral rolandic operculum, left superior/inferior parietal, and lingual gyri, the number of such samples was relatively small 29 . Consequently, we hypothesized that volumetric changes might be insufficiently sensitive than microstructural alterations based on DKI, or that microstructural changes occur earlier than GM atrophy. These values also suggested that DKI was a noninvasive and flexible method to detect SD impairment, and a potential imaging marker to recognize SD patients. In the present study, the F0, shimmer, and jitter of the control group were statistically lower than those of the SD group. These parameters indicated deterioration in the quality of the patients’ voices. The SD group had higher VHI and lower V-RQOL scores, indicating that the patients’ auditory perception and voice-related quality of life were significantly decreased. In addition, the G, R, B, A, and S of the control group were statistically significantly lower than those of the SD group, showing that the general severity, roughness, and tension of voice disorders in the SD group were higher than those in the control group. In our studies, the FA and MD of the frontal region had inverse correlation with jitter values; the FA in the cingulum and the MD in the temporal had negative correlations with G; the FA in the frontal region also had negative correlations with A. Previous studies have reported that measures of water diffusivity using DTI were associated with the symptoms of SD, such as the number of voice breaks in sentences 16 . Therefore, our findings coupled with autopsy results of focal abnormalities in an SD patient and the loss of the voluntary control of voice production indicated that microstructural changes may potentially represent primary brain alterations. Several limitations deserve consideration in this study. First, our research did not evaluate functional connectivity, so it was unable to match structural changes with functional variations. Second, if the sample size was larger, it is possible that a more positive correlation would be evident between DKI and clinical characteristics of our participants. Third, we used three b values in this study, the accuracy of these measures might improve, if more b values were used. Conclusions In conclusion, our findings suggested that altered microstructural integrity, as reflected by DKI parameters, may represent neurological changes in SD patients. The slow progressive neurodegeneration in some regions could be responsible for abnormalities in microstructural organization, thereby contributing to the pathophysiology of SD. Moreover, DKI has the potential to serve as a noninvasive and quantitative tool for detecting microstructural variations, offering promising insights that complement morphological evidence. Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. 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Supplementary Files Table2.docx Cite Share Download PDF Status: Published Journal Publication published 29 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviewers agreed at journal 12 Mar, 2025 Reviews received at journal 12 Mar, 2025 Reviewers agreed at journal 09 Mar, 2025 Reviewers invited by journal 08 Mar, 2025 Editor assigned by journal 08 Mar, 2025 Editor invited by journal 05 Mar, 2025 Submission checks completed at journal 04 Mar, 2025 First submitted to journal 24 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6097068","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424295535,"identity":"a0d3ba71-00a5-4bfd-9761-23d6a32dfeba","order_by":0,"name":"Fuzhuang Jiang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fuzhuang","middleName":"","lastName":"Jiang","suffix":""},{"id":424295536,"identity":"8817c141-b87f-4bee-bba0-bbffdbf307e2","order_by":1,"name":"Shizhen Zou","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shizhen","middleName":"","lastName":"Zou","suffix":""},{"id":424295537,"identity":"0eb42416-50f1-497f-99d8-0ac4c5400cc4","order_by":2,"name":"Chunjie Wang","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunjie","middleName":"","lastName":"Wang","suffix":""},{"id":424295540,"identity":"e8a5b64f-49d0-448e-8d46-255e5c8dd446","order_by":3,"name":"Yang Liu","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":424295543,"identity":"57c3b5c5-a92f-491e-9b73-ac0c145dbb92","order_by":4,"name":"Jinrang Li","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jinrang","middleName":"","lastName":"Li","suffix":""},{"id":424295546,"identity":"b70efdd6-9481-4030-a8e6-b972462d6735","order_by":5,"name":"Liuquan Cheng","email":"","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liuquan","middleName":"","lastName":"Cheng","suffix":""},{"id":424295548,"identity":"6cd7d68d-2451-4217-9c7e-1c836a480023","order_by":6,"name":"Dongyan Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACAzDJxmDHxsx84MCHHyRoSeZnZ0s8OLOHBC2MM/t5jA9zsBGhxVwi+dnDL2WHmQ0O83w4zMDDIM8vdgC/FssZaebGMucO8xkc5t1wuMCCwXDm7AQCDruRYCYt2QayBahlBg9DgsFtglrSv4G0MG44zPPgMA8bUVpyzCQ/ArXMbOZhIFLLmTdl0gzn0pP5mdkMgIEsQYRfjqdvk/xRZm3Hxn/48YcPP2zk+aUJaAEBZh6GZhhbgrByEGD8wVBHnMpRMApGwSgYmQAAz0BGbAOJkugAAAAASUVORK5CYII=","orcid":"","institution":"Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Dongyan","middleName":"","lastName":"Huang","suffix":""}],"badges":[],"createdAt":"2025-02-24 12:53:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6097068/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6097068/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-18157-w","type":"published","date":"2025-09-29T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78241400,"identity":"070bfd2d-7e97-4a50-9654-1e3770c88d86","added_by":"auto","created_at":"2025-03-11 09:04:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2576577,"visible":true,"origin":"","legend":"\u003cp\u003eTypical DKI-derived maps in a SD patient (female, 54 years old). FA, fractional anisotropy; MD ,mean diffusion; MK, mean kurtosis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6097068/v1/5efe013807978464dc397853.png"},{"id":78243598,"identity":"084e8692-07db-4b8e-b17c-4f064040f8c2","added_by":"auto","created_at":"2025-03-11 09:12:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1976302,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical maps of reduced FA in patients with SD compared with controls are overlaid in a T1 template. Stereotaxic Z coordinates of the axial slices are displayed in gray. Images are shown in neurological convention (the right side of the image corresponds to the right side of the brain).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6097068/v1/c28a28e8e3a31e328b5e60ba.png"},{"id":92883821,"identity":"3ac93e83-c632-4867-a418-b54fe4ed6855","added_by":"auto","created_at":"2025-10-06 16:10:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4867629,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6097068/v1/532b9156-3832-4cff-b5f8-4f143dbd5066.pdf"},{"id":78243599,"identity":"873cebf4-3b8d-4a7a-bca5-c15ca29c1b42","added_by":"auto","created_at":"2025-03-11 09:12:39","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18774,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6097068/v1/6700f33d67906ac11bd6dff6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of brain microstructural abnormalities in spasmodic dysphonia: A preliminary diffusion kurtosis imaging study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpasmodic dysphonia (SD) is the laryngeal form of focal dystonia which is characterized by involuntary spasms of the laryngeal muscles in speech\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. And it is also a task-specific disability, affecting voice production only during speech, but not during vocal expressions of emotions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. SD has a low prevalence and predominately affects females\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. SD presents with different clinical manifestations, with the most prevalent subtypes being adductor and abductor SD phenotypes, which are characterized by involuntary spasms in the laryngeal muscles during speech\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe precise etiology and pathophysiology of SD is still unclear. Roberts' study provided the first clinical evidence that SD is a speech disorder resulting from a neurological dysfunction, or possibly a structural neurological disorder\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.The histopathologic and magnetic resonance imaging (MRI) findings of recurrent laryngeal nerve demyelination and infarcts or demyelinating lesions of the brain further indicated that SD is a central nervous system disorder\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. A number of studies\u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, using various functional brain imaging techniques, have provided compelling evidence that the disorder is associated with structural and abnormal activities involving the speech sensorimotor network. Voxel-based morphometry revealed that patients with SD have abnormalities in critical structures of the speech control system, including the laryngeal sensorimotor cortex, and in structures commonly abnormal in other primary dystonia, with increased gray matter (GM) volume and cortical thickness\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Furthermore, functional neuroimaging studies of SD have identified abnormal activities (increases/decreases) in central pathways required for control of learned voice production\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. For example, functional neuroimaging studies of SD patients have reported hyperactivation in the auditory-related areas and motor speech areas. The left primary sensory area and bilateral subcortical nucleus had lower activation\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe presence of functional abnormalities suggests that brain structures may be altered in SD patients. Carbon et al\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e used diffusion tensor imaging (DTI) and reported that fractional anisotropy (FA) was reduced in the sub-gyral white matter (WM) of the sensorimotor cortex of DYT1 carriers, which may contribute to the susceptibility of DYT1 carriers to develop clinical manifestations of dystonia. DTI demonstrated that the FA was decreased in the right genu of the internal capsule, and the overall water diffusivity was increased in the bilateral corticobulbar/corticospinal tract in SD patients\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Furthermore, water diffusivity was increased in cerebellar white matter and GM. It was also revealed that diffusivity changes were associated with the clinical manifestations of SD. In conclusion, previous studies have indicated that brain abnormalities may modify the central control of voluntary voice production and could contribute to the pathophysiology of SD.\u003c/p\u003e \u003cp\u003eHowever, the above results indicated inconsistencies in directional diffusivities, which may be attributed to small cohort sizes or inherent limitations of the DTI model. DTI utilizes free water diffusion, also known as Gaussian diffusion, to provide in vivo quantitative assessments of microstructural integrity and organization\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Owing to the barriers such as organelles and cell membranes, however, water molecules tend to exhibit non-Gaussian diffusion. Thus, the sensitivity of DTI in evaluating microstructural integrity is probably not entirely optimal. Diffusional kurtosis imaging (DKI) can quantify non-Gaussian diffusion characteristics, with image acquisition involving higher b-values (\u0026ge;\u0026thinsp;2,000 s/mm\u003csup\u003e2\u003c/sup\u003e), as well as more gradient orientations directions, and may involve more exact and specific parameters to characterize the brain microstructure\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, particularly in brain regions of high tissue heterogeneity such as GM\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Furthermore, DKI demonstrates greater sensitivity and specificity in evaluating developmental and pathological alterations in neural tissues, and consequently may be a useful and noninvasive indicator of brain tissues affected by various diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe purpose of our study was to characterize brain tissue microstructural damage and to estimate the performance of the DKI in detecting GM and WM abnormalities in SD patients. We concurrently analyzed the correlation between DKI parameters and clinical examination in SD patients to evaluate the potential of these metrics as supplementary indicators of intracranial disease severity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eStudy participants\u003c/h2\u003e\n \u003cp\u003eParticipants included 20 patients with SD (all females; mean age = 44.41 years; range, 23–68 years) and 20 healthy volunteers (all females; mean age = 42.50 years; range, 24–66 years) serving as controls. All 40 subjects were strictly right-handed.\u003c/p\u003e\n \u003cp\u003eEstablishment of a definitive SD diagnosis based on the criteria of Ludlow et al\u003csup\u003e23\u003c/sup\u003e. SD was diagnosed by otolaryngologic examinations and speech/language assessment. Inclusion criteria were: (1) no developmental variants in the larynx; (2)no botulinum toxin injections for SD or poor improvement after voice therapy; (3) no previous treatment with neuroleptic drugs. None of participants had a past or present history of any neurological (except SD), psychiatric or laryngeal disorders, intracranial vascular malformations, participants with normal MRI scans were recruited from among the spouses of patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEvaluation\u003c/h3\u003e\n\u003cp\u003eAcoustic analysis was conducted in a standard quiet voice testing room using a XION DiVAS v2.3 and a head wearing microphone. A long /a/ vowel was recorded in a tone by adjusting the distance between the patient's mouth and the microphone to 30 cm. The parameters of the fundamental frequency (F0), jitter (%) and shimmer (%) were recorded.\u003c/p\u003e\n\u003cp\u003eThe subjective evaluations included auditory perception grading(GRBAS) and two of patient self-assessment tools Voice Handcap Index(VHI)and Voice-Related Quality of Life Scale (V-RQOL).\u003c/p\u003e\n\u003cp\u003eGRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) is a five-dimensional scale auditory perception assessment system, which was scored on a 4-point scale\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eVoice Handicap Index (VHI) is a valid tool which is commonly used scale to measure voice handicap, with three information on the functional, emotional and physical attributes of the voice disorder. The participants scored five points for each question\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eV-RQOL assesses each participant’s voice-related quality of life, which comprises ten items in a physical functioning and social-emotional\u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe patients underwent MR examination using 3T MR (Discovery MR750, GE Healthcare, Waukesha, WI, USA) with 8-channel head phased-array coils. High-resolution anatomical images were obtained with axial T1-weighted three-dimensional brain volume imaging sequences. The sequence parameters are shown in Table 1. b values = 0, 1000, and 2000 s/mm\u003csup\u003e2\u003c/sup\u003e ; 30 diffusion encoding directions for each nonzero b value; and scan time 10 min and 35 s.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMRI protocol.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSequences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTR (ms)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTE (ms)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNEX\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlice Thickness (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFOV (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMatrix\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003eWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 × 240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352 × 352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003eWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 × 240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e256 × 256\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240 × 240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 × 128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eLabel: SE = spin echo, FSE = fast spin echo, TR = repetition time, TE = echo time, NEX = number of excitations, FOV = field of view, DWI = diffusion-weighted imaging.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n\u003c/div\u003e\n\u003ch3\u003eImage processing and analysis\u003c/h3\u003e\n\u003cp\u003eDKI parameters (FA, MD, and MK) were measured and processed using python3.11 (https://www.python.org) and MATLAB R2018b (https://www.mathworks.com/products/matlab.html). DKI data were first preprocessed by denoising. The DKI data were first stripped for each participant. Tract-based spatial statistics (TBSS) were implemented using the Dipy (https://dipy.org/) to alleviate partial volume effects. We used the skeleton projection step of TBSS to analyze the WM and GM. First, we aligned all FA images to the standard AAL_61x73x61_YCG.nii. T1-weighted images were registered to AAL standard-space images to provide high resolution, anatomical references for the automatic delineation of regions of interest. Then, other images in the standard space were averaged, and the mean FA skeleton was used as a representation of the centers of all tracts common to the groups. Finally, each participant's images were projected onto the mean skeleton, and the MK and MD images were presented onto the mean FA skeleton. MATLAB R2018b was used to extract FA, MK, and MD values.\u003c/p\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analyses were conducted with SPSS Statistics 26.0 software (IBM, Armonk, NY, USA). Continuous variables are described as mean ± standard deviation. The Student's t-test for normally distributed continuous variables, the Mann–Whitney test for non-normally distributed continuous variables, and the chi-square test or the Fisher’s exact test for categorical variables. Differences with a corrected p-value of less than 0.05 were considered significant. Non-parametric Spearman or parametric Pearson correlation coefficient are used to identify correlations between the DKI parameters. Both Spearman and Pearson correlation coefficients are interpreted according to Cohen et al\u003csup\u003e27\u003c/sup\u003e: (1) 0.10–0.29 represents a weak correlation; (2) 0.30–0.49 represents a moderate correlation; and (3) \u0026gt; 0.50 represents a strong correlation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional review board (No.HZKY-PJ-2023-32). All participants provided written informed consent according to the guidelines of the Ethics Committee. The methods in the current study were performed in accordance with the relevant guidelines and regulations.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eClinical factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe SD group comprised 20 subjects (all females; mean age=44.41 years; range, 23\u0026ndash;68 years). The control group consisted of 20 subjects (all females; mean age=42.50 years; range, 24\u0026ndash;66 years). The baseline clinical factors, acoustic voice analysis measurements, and auditory-perceptual analysis findings of SD patients and control subjects are reported in table 2. All controls were sex-matched to patients. There was no significant difference between the patients and controls according to age (\u003cem\u003eZ\u003c/em\u003e=\u0026ndash;0.108, P=0.914).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eKurtosis parameters from the DKI\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the parametric maps of FA, MK, and MD from SD subjects. The results indicated a significant decrease in FA in patients with SD, when compared with controls (P\u0026lt;0.05). Decreases in FA in SD patients were observed in the GM of the left inferior temporal gyrus, anterior and posterior cingulate cortices, caudate nucleus, right inferior frontal operculum, as well as in the WM of the left precentral gyrus, superior frontal gyrus, supplementary motor area (SMA), fusiform gyrus, angular gyrus, posterior cingulate gyrus, middle temporal gyrus, and the right superior temporal pole and both anterior cingulate gyrus, inferior frontal operculum gyrus, caudate nucleus, thalamus, and inferior temporal gyrus (Figure 2).\u003c/p\u003e\n\u003cp\u003eCompared with control subjects, SD patients had significantly increased MD (P\u0026lt;0.05) and MK in multiple GM and WM regions. The MD was increased in SD located in the WM within the left inferior occipital gyrus, superior frontal orbital gyrus, inferior temporal gyrus, fusiform gyrus, and the right lingual gyrus. The MK were increased in SD located in the GM within the left lingual gyrus and the right inferior frontal operculum gyrus, inferior frontal orbital gyrus, rolandic operculum, superior frontal orbital gyrus, middle frontal orbital gyrus, medial frontal orbital gyrus, rectus gyrus, and in the WM in the left thalamus, superior temporal gyrus, anterior cingulate gyrus, and the right precentral gyrus, superior frontal gyrus, inferior frontal operculum gyrus, superior medial frontal gyrus, and putamen.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelations between diffusion parameters and clinical scores\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the WM, the FA and MD of Frontal_Sup_L and jitter values, the MD of Frontal_Sup_Orb_L and jitter values had significant inverse correlations, which were greater in SD patients \u003cem\u003e(r\u003c/em\u003e=\u0026ndash;0.565, P=0.009; \u003cem\u003er\u003c/em\u003e=\u0026ndash;0.460, P=0.041, respectively) than in the control group. In the WM, the FA and MD in regions that differed between the two groups (FA in the Cingulum_Ant_L and MD in the Temporal_Inf_L) were also examined for relationships with numbers of G in SD patients. We found negative correlations between the FA, MD, and G (\u003cem\u003er\u003c/em\u003e=\u0026ndash;0.474, P=0.035; \u003cem\u003er\u003c/em\u003e=\u0026ndash;0619, P=0.004, respectively) in SD patients; and the FA in Frontal_Sup_L also had negative correlations with A \u003cem\u003e(r\u003c/em\u003e=\u0026ndash;0.627, P=0.003).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study made significant observations regarding WM and GM microstructural changes in SD patients using DKI. The analysis revealed that SD is associated with disruptions in the normal architecture of WM and GM. In comparison to controls, SD patients had abnormalities, reflected by decreased FA, and increased MD and MK, in several WM and GM areas, especially within the frontal, temporal, caudate nucleus, and SMA. Our findings are consistent with neuroimaging studies showing that SD consistently showed abnormalities in the sensorimotor cortex, basal ganglia, thalamus, WM, and GM connections\u003csup\u003e28,29\u003c/sup\u003e. Additionally, we identified correlations between changes in WM diffusion metrics and the severity of SD symptoms. Therefore, the brain abnormalities identified in this study can be considered primary alterations underlying the pathophysiology of SD.\u003c/p\u003e\n\u003cp\u003eWidespread WM differences were found in SD patients as indicated by the FA, which was consistent with the results of previous studies\u003csup\u003e30,31\u003c/sup\u003e. DTI findings and regional microstructural abnormalities were found in the thalamus, caudate nucleus, cingulum bundle, and temporal lobe\u003csup\u003e16,29\u003c/sup\u003e. Our study provides a thorough outline of WM changes in SD patients through DKI parameters derived using a global analysis approach. FA gauges the extent of anisotropy of water molecules and indicates preferential movement of water molecules in one direction. The water molecules diffuse through neural fibers in the brain, particularly via axonal tracts, so the FA is considered an indirect measure of axonal integrity\u003csup\u003e32,33\u003c/sup\u003e. The regions where FA were reduced in SD patients represented positions where the molecular movement within axonal fibers was not as structured and homogeneous as in control subjects, therefore corresponding to changes in WM integrity and axonal impairment\u003csup\u003e18\u003c/sup\u003e. While the physiological correlates of abnormal FA remain speculative, these brain alterations have been confirmed through focal histopathological analyses, which identified reduced WM density due to decreased myelin content and scattered microglial/macrophage activation in SD patients\u003csup\u003e16\u003c/sup\u003e. The idiosyncratic character of the brain microstructural abnormalities substantiated their confinement to the corticobulbar/corticospinal tract and their primary input/output structures, suggesting an association with SD.\u003c/p\u003e\n\u003cp\u003eIt should be noted that in our results of SD, FA in the left supplementary motor area (SMA) was reduced. SMA is situated in Brodmann\u0026rsquo;s area 6 of the frontal lobe, and plays an important role in motor initiation, while the pre-SMA has a significant role in behavioral energization and inhibition\u003csup\u003e34\u003c/sup\u003e. According to an MRI study of Pool et al, a majority of SD patients have rapid alternating motion (RAM) and abnormal limb rhythms. As RAM impairment might involve the basal ganglia or upper motor neuron pathways, it has been demonstrated that involvement of a pallidothalamic-SMA system could be present in SD patients\u003csup\u003e35\u003c/sup\u003e. Moreover, MRI findings from a patient with adductor-type SD showed that the SMA was not activated during the phonation task\u003csup\u003e12\u003c/sup\u003e. The above findings suggest a defective SMA function in SD patients, which ought to be intimately associated with the generation of pathogenic voice. Reduced FA values in the SMA region in SD patients suggest the presence of microstructural abnormalities.\u003c/p\u003e\n\u003cp\u003eIn comparison to DTI, DKI has been effective for monitoring microstructural changes by using additional kurtosis metrics\u003csup\u003e36\u003c/sup\u003e. MD values represent the apparent diffusion coefficient corrected for non-Gaussian distribution\u003csup\u003e36\u003c/sup\u003e. MD is also considered to be an indirect measure of axonal integrity\u003csup\u003e32,33\u003c/sup\u003e. The alteration of MD is probably associated with the decreased cellularity. And the diffusion variations may be related to changes in the space available for extracellular water. As a result, the increased MD is hypothesized to be associated with a larger extracellular space. Our results showed increased MD in SD, particularly in the WM of the left inferior occipital gyrus, superior frontal orbital gyrus, inferior temporal gyrus, and fusiform gyrus, consistent with findings by Bonilha et al\u003csup\u003e37\u003c/sup\u003e. They reported that idiopathic dystonia was related to the increase in MD in adjacent WM, bilateral to the pallidum and putamen, and in subcortical hemispheric regions. The changes of MD may be related with cellular membrane breakdown, cell death, and tissue cavitation\u003csup\u003e38\u003c/sup\u003e. In the present study, changes of MK had a broader distribution than that of MD. MK is the average value of kurtosis over all possible gradient directions, which can indicates variations in structural complication\u003csup\u003e39,40\u003c/sup\u003e. Previous studies have reported that MK reflected neuronal shrinkage\u003csup\u003e40\u003c/sup\u003e, changes in axonal and myelin density\u003csup\u003e41\u003c/sup\u003e, and astrogliosis\u003csup\u003e42\u003c/sup\u003e. An animal study also reported that MK closely linked to neurite density in the caudate putamen\u003csup\u003e43\u003c/sup\u003e. Thus, our study further indicated that DKI was more sensitive to alterations in regions of WM microstructure in SD patients.\u003c/p\u003e\n\u003cp\u003eWM involves the linkages between encephalic areas, and is accountable for information exchange and communication between GM. WM abnormalities are recognized as the consequence of Wallerian degeneration, which is secondary to the loss of neurons in the GM\u003csup\u003e44-46\u003c/sup\u003e. We observed lower FA in the frontal lobe and higher MK values in the temporal lobe of patients with SD. FA and MK represent the overall microstructure complexity of brain tissue. Therefore, changes in FA and MK represent alterations in GM microstructure, which may result from neurodegeneration, involving decreased dendrite spines and cell densities\u003csup\u003e18,47\u003c/sup\u003e. Such variations will not automatically induce observable volumetric variation in structural imaging. While several studies identified the patients with SD have decreased cortical areas in the bilateral rolandic operculum, left superior/inferior parietal, and lingual gyri, the number of such samples was relatively small\u003csup\u003e29\u003c/sup\u003e. Consequently, we hypothesized that volumetric changes might be insufficiently sensitive than microstructural alterations based on DKI, or that microstructural changes occur earlier than GM atrophy. These values also suggested that DKI was a noninvasive and flexible method to detect SD impairment, and a potential imaging marker to recognize SD patients.\u003c/p\u003e\n\u003cp\u003eIn the present study, the F0, shimmer, and jitter of the control group were statistically lower than those of the SD group. These parameters indicated deterioration in the quality of the patients\u0026rsquo; voices. The SD group had higher VHI and lower V-RQOL scores, indicating that the patients\u0026rsquo; auditory perception and voice-related quality of life were significantly decreased. In addition, the G, R, B, A, and S of the control group were statistically significantly lower than those of the SD group, showing that the general severity, roughness, and tension of voice disorders in the SD group were higher than those in the control group. In our studies, the FA and MD of the frontal region had inverse correlation with jitter values; the FA in the cingulum and the MD in the temporal had negative correlations with G; the FA in the frontal region also had negative correlations with A. Previous studies have reported that measures of water diffusivity using DTI were associated with the symptoms of SD, such as the number of voice breaks in sentences\u003csup\u003e16\u003c/sup\u003e. Therefore, our findings coupled with autopsy results of focal abnormalities in an SD patient and the loss of the voluntary control of voice production indicated that microstructural changes may potentially represent primary brain alterations.\u003c/p\u003e\n\u003cp\u003eSeveral limitations deserve consideration in this study. First, our research did not evaluate functional connectivity, so it was unable to match structural changes with functional variations. Second, if the sample size was larger, it is possible that a more positive correlation would be evident between DKI and clinical characteristics of our participants. Third, we used three b values in this study, the accuracy of these measures might improve, if more b values were used.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our findings suggested that altered microstructural integrity, as reflected by DKI parameters, may represent neurological changes in SD patients. The slow progressive neurodegeneration in some regions could be responsible for abnormalities in microstructural organization, thereby contributing to the pathophysiology of SD. Moreover, DKI has the potential to serve as a noninvasive and quantitative tool for detecting microstructural variations, offering promising insights that complement morphological evidence.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBlitzer A, et al. Botulinum toxin management of spasmodic dysphonia (laryngeal dystonia): a 12-year experience in more than 900 patients. \u003cem\u003eThe Laryngoscope\u003c/em\u003e.\u003cstrong\u003e108\u003c/strong\u003e(10),1435-1441(1998).\u003c/li\u003e\n\u003cli\u003eBloch C, et al. 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Essential Tremor: A Common Disorder of Purkinje Neurons? \u003cem\u003eThe Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry\u003c/em\u003e.\u003cstrong\u003e22\u003c/strong\u003e(2),108-118(2016).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Spasmodic dysphonia, Magnetic Resonance Imaging, Diffusion Kurtosis Imaging, Neuroimaging, Voice Assessment","lastPublishedDoi":"10.21203/rs.3.rs-6097068/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6097068/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo investigate microstructural changes in spasmodic dysphonia (SD) patients applying diffusion kurtosis imaging (DKI) data. DKI (b values\u0026thinsp;=\u0026thinsp;0, 1,000, and 2,000 s/mm\u003csup\u003e2\u003c/sup\u003e) was performed for 20 SD patients and 20 controls. DKI parameters including kurtosis fractional anisotropy (FA), mean diffusion (MD), and mean kurtosis (MK). Python3.11 and MATLAB R2018b with tract-based spatial statistics were used to compare group differences. The correlation analysis was used to assess the correlation between changes in FA and clinical measures. Compared to healthy controls, SD patients showed significantly reduced FA and increased MD in the white matter (WM) and gray matter (GM). Increases of MK had broader distributions than MD. In the WM, the FA and MD of frontal and jitter values had significant inverse correlations in SD patients. The FA and MD were negative correlated with grade, and the FA in the frontal region had a negative correlation with asthenia. Patients with SD exhibited microstructural changes in brain regions in charge of motor conduction and auditory functions. The WM diffusion metric changes had negative correlations with clinical symptoms of SD. The brain changes could modify the central control of voluntary vocalizations and may consequently be the pathophysiologic reason for SD.\u003c/p\u003e","manuscriptTitle":"Characterization of brain microstructural abnormalities in spasmodic dysphonia: A preliminary diffusion kurtosis imaging study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 09:04:34","doi":"10.21203/rs.3.rs-6097068/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-16T07:03:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T17:46:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"140770165265910778225616135701258193733","date":"2025-03-12T11:55:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-12T07:42:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264141403682974327650028556161268508829","date":"2025-03-09T14:49:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-08T19:13:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-08T19:11:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-05T05:35:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-04T08:15:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-02-24T12:46:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1fc2df9c-b8dd-4ccc-b4d6-de487a639b53","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":45214267,"name":"Health sciences/Neurology/Neurological disorders/White matter disease"},{"id":45214268,"name":"Health sciences/Neurology/Neurological disorders"}],"tags":[],"updatedAt":"2025-10-06T16:03:37+00:00","versionOfRecord":{"articleIdentity":"rs-6097068","link":"https://doi.org/10.1038/s41598-025-18157-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-09-29 15:57:03","publishedOnDateReadable":"September 29th, 2025"},"versionCreatedAt":"2025-03-11 09:04:34","video":"","vorDoi":"10.1038/s41598-025-18157-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-18157-w","workflowStages":[]},"version":"v1","identity":"rs-6097068","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6097068","identity":"rs-6097068","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}