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. Author manuscript; available in PMC: 2025 Feb 1.
Published in final edited form as: Cortex. 2023 Nov 8;171:272–286. doi: 10.1016/j.cortex.2023.08.019

Diffusion Tensor Imaging-Based Multi-Fiber Tracking Reconstructions Can Regionally Differentiate Phonetic versus Prosodic Subtypes of Progressive Apraxia of Speech

Rodolfo G Gatto 1, Peter R Martin 2, Rene L Utianski 1, Joseph R Duffy 1, Heather M Clark 1, Hugo Botha 1, Mary M Machulda 3, Keith A Josephs 1, Jennifer L Whitwell 4,#
PMCID: PMC10922200  NIHMSID: NIHMS1944020  PMID: 38061209

Abstract

Two subtypes of progressive apraxia of speech (PAOS) have been recognized: phonetic PAOS (PAOS_ph) where speech output is dominated by distorted sound substitutions and prosodic PAOS (PAOS_pr) which is dominated by segmented speech. We investigate whether these PAOS subtypes have different white matter microstructural abnormalities measured by diffusion tensor tractography. Thirty-three patients with PAOS (21 PAOS_ph and 12 PAOS_pr) and 19 healthy controls were recruited by the Neurodegenerative Research Group (NRG) and underwent diffusion MRI. Using a whole-brain tractography approach, fractional anisotropy (FA) and mean diffusivity (MD) were extracted for cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical white matter tracts. A hierarchical linear model was applied to assess tract-level FA and MD across groups. Both PAOS_ph and PAOS_pr showed degeneration of cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical white matter tracts compared to controls. However, degeneration of the body of corpus callosum, superior thalamic radiation, and superior cerebellar peduncle was greater in PAOS_pr compared to PAOS_ph, and degeneration of the inferior segment of the superior longitudinal fasciculus (SLF) was greater in PAOS_ph compared to PAOS_pr. Worse parkinsonism correlated with greater degeneration of cortico-cortical and cortico-subcortical tracts in PAOS_ph. Apraxia of speech articulatory error score correlated with degeneration of the superior cerebellar peduncle tracts in PAOS_pr. Phonetic and prosodic PAOS involve the compromise of a similar network of tracts, although there are connectivity differences between types. Whereas clinical parameters are the current gold standard to distinguish PAOS subtypes, our results allege the use of DTI-based tractography as a supplementary method to investigate such variants.

Keywords: progressive apraxia of speech, 4R tauopathy, diffusion tensor imaging, tractography, connectomics

1. Introduction

Progressive apraxia of speech (PAOS) is a clinical syndrome characterized by an impairment in speech motor planning and is most commonly associated with a 4-repeat tauopathy (4RT) (Duffy, Utianski, & Josephs, 2021; Keith. Josephs et al., 2021; Keith Josephs et al., 2012). Some patients with PAOS also present with agrammatic aphasia. Previous studies using multimodal imaging approaches have shown that PAOS patients have bilateral frontal grey matter (GM) loss, particularly located in the premotor and supplementary motor areas (SMA), as well as bilateral frontal white matter (WM) degeneration underlying these regions and involving the corpus callosum and white matter connections from the SMA (H. Botha et al., 2015; Keith Josephs et al., 2013; Keith Josephs et al., 2012; Valls Carbo et al., 2022). In addition, previous functional MRI (fMRI) studies assessing brain connectivity have demonstrated reduced connectivity between the SMA and the rest of the speech and language network (Hugo Botha et al., 2018) and shown that connectivity from the premotor cortex is associated with rates of brain neurodegeneration (Sintini et al., 2022). These findings suggest that breakdowns in WM interconnectivity are important components of the disease in PAOS.

The specific speech characteristics observed in patients with PAOS vary, and two subtypes have been described based on the relative prominence of phonetic or prosodic speech patterns (Keith Josephs et al., 2013; Rene Utianski et al., 2018). Phonetic PAOS (paos_ph) is characterized predominantly by distorted sound substitutions and additions, while prosodic PAOS (paos_pr) is characterized predominantly by slow, prosodically segmented speech (Duffy et al., 2021; Rene Utianski et al., 2018). We have shown that these two subtypes differ in their clinical evolution (Whitwell et al., 2017) and differ in underlying pathology, with phonetic PAOS more commonly associated with corticobasal degeneration and prosodic PAOS associated with progressive supranuclear palsy; both 4RT (Keith Josephs et al., 2021). On neuroimaging, both subtypes were associated with atrophy of the SMA, although PAOS_ph showed additional involvement of the prefrontal cortex and cerebellum (Rene Utianski et al., 2018). A voxel-based diffusion tensor imaging (DTI) analysis showed degeneration of the white matter underlying the SMA in both subtypes, with additional involvement of the body of the corpus callosum and cingulum in paos_ph and involvement of the superior cerebellar peduncle (SCP), likely related to the underlying progressive supranuclear palsy pathology, in paos_pr (Rene Utianski et al., 2018). However, no differences were identified between the two subtypes on direct comparison, suggesting that more sensitive methods are needed to identify patterns of structural connectivity disruption in PAOS subtypes. As such, studies based on neuropathological data suggested that differences between PAOS subtypes may be observed in cortical-striatal-pallido-Nigro-luysial networks (Keith. Josephs et al., 2021).

Diffusion tensor imaging has the advantage of approximating the reconstruction of the WM tracts allowing specific interrogation of WM tracts in the brain, such as those connecting specific regions supporting speech and language (Jeurissen, Descoteaux, Mori, & Leemans, 2019; Smits, Jiskoot, & Papma, 2014). A recent DTI tractography study in PAOS demonstrated abnormal diffusion in tracts connecting the SMA with the putamen, prefrontal cortex, Broca’s area (frontal aslant tract), and motor cortex, and showed that degeneration of SMA to motor cortex tracts is associated with apraxia of speech severity (Carbo et al., 2022). However, this study only assessed WM tracts from the SMA and did not assess differences between PAOS subtypes. Other studies have assessed diffusion tractography in patients with agrammatic aphasia (Catani et al., 2013; Mandelli et al., 2014), although these studies have not considered the role of apraxia of speech or PAOS subtypes.

Therefore, our study expands on previous tractography results by applying a multi-tract approach and specifically assessing differences between clinically defined PAOS subtypes. We hypothesized that the unique clinical presentations of PAOS subtypes will have specific patterns of WM microstructural degeneration. In addition, we propose that microstructural characteristics from each PAOS subtype would relate to distinct clinical features.

2. Materials and Methods

We report how we determined our sample size, all data exclusions, all inclusion/exclusion criteria, whether inclusion/exclusion criteria were established prior to data analysis, all manipulations, and all measures in the study.

Patients

From February 2018 to September 2021, a total of 33 patients with PAOS were recruited by the Neurodegenerative Research Group (NRG) at Mayo Clinic, Rochester, MN, and underwent detailed speech and language, neurological evaluations, and a 3T MRI acquired in a Siemens system. These PAOS patients were clinically subdivided into two groups: paos_ph (n=21) and paos_pr (n=12), as described in more detail below. An additional group of 20 age and sex-matched healthy controls were also recruited for reference purposes. Controls were included if they did not have any complaints of cognitive, motor, or behavioral abnormalities. In addition, patients with neuroimaging studies showing gross neuroanatomical deformities were excluded from all groups. The study was approved by the Mayo Clinic IRB and informed consent from patients or direct relatives was obtained from all participants.

2.2. Clinical evaluations

All patients underwent a detailed speech and language evaluation performed by a board-certified speech-language pathologist (JRD, HMC, or RLU) that has been previously described in detail (Keith. A. Josephs et al., 2012). Apraxia of speech severity was assessed using the Apraxia of Speech Rating Scale-version-3 (ASRS) (Clark et al., 2021; Strand, Duffy, Clark, & Josephs, 2014), which includes subscores for phonetic and prosodic speech characteristics, and an Articulatory Error Score (AES) (Utianski et al., 2021). Aphasia severity was assessed using the Western Aphasia Battery (WAB) Aphasia Quotient (WAB-AQ) (Clark et al., 2020). Judgments about motor speech abilities were based on all spoken language tasks of the WAB plus additional speech tasks that included vowel prolongation, speech-like alternating motion rates, speech-like sequential motion rates, word and sentence repetition tasks, and a conversational speech sample. The same speech tasks were also judged for the presence or absence of dysarthria, which was rated on a 0–4 severity scale. Patients were diagnosed with PAOS if there was evidence of apraxia of speech, either with or without concomitant aphasia. Dysarthria could be present but had to be less severe than apraxia of speech. Judgment of the PAOS subtype was based on characteristics present during spontaneous speech and structured speech tasks, and judgments were made blinded to all neurological, neuropsychological, and imaging findings (R. L. Utianski et al., 2018). A designation of PAOS_ph was made if the predominant characteristics of the AOS were distorted sound substitutions, deletions, or additions. A designation of PAOS_pr was made if the predominant characteristics of the AOS were lengthened inter-segment durations between syllables within words, between words, or both. Neurologic testing was conducted by a behavioral neurologist (KAJ or HB) and included assessments to characterize general cognitive ability by a Montreal Cognitive Assessment (MoCA) (Nasreddine et al., 2005), and frontal lobe function by the Frontal Assessment Battery (FAB) (Dubois, Slachevsky, Litvan, & Pillon, 2000). Motor impairments were assessed by the Movement Disorders Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale motor subsection (MDS-UPDRS III) (Goetz et al., 2008). Legal copyright restrictions prevent public archiving of MoCA, FAB, MSD - UPDRS III, WAB AQ, ASRS_v3 Total and AES_ttl_pct_err test, which can be obtained from the copyright holders in the cited references.

2.3. Imaging data acquisition and preprocessing

Patients underwent diffusion-weighted MRI (dMRI) with a 3T SIEMENS scanner. The imaging protocol included a 3D Magnetization Prepared Rapid Acquisition Gradient-Echo (MPRAGE) sequence as well as a dMRI sequence with a spin-echo single shot Echo Planar Imaging (EPI) sequence. The diffusion data consisted of 127 volumes with 13 non-diffusion-weighted images (b = 0 s/mm2), and 114 diffusion-encoding gradient directions (6 b = 500, 48 b = 1000, and 60 b = 2000s/ mm2).TE was set at 71 ms and TR=3400 ms. The in-plane resolution was set to 2 mm. The slice thickness for each section was 2 mm. Images were corrected for head motion, eddy current, and susceptibility artifact distortions were corrected using standard algorithms extensively described in the literature (J. L. R. Andersson, Graham, Drobnjak, Zhang, & Campbell, 2018; Jesper L. R. Andersson & Sotiropoulos, 2016). The b-table was checked by an automatic quality control routine to ensure its accuracy (Schilling et al., 2019). The accuracy and reliability of the diffusion data were also checked by incorporated algorithms (F. C. Yeh, Zaydan, et al., 2019).

2.4. DTI calculations

A diffusion tensor model was estimated and subsequently used to calculate fractional anisotropy (FA) and mean diffusivity (MD) employing an open-source software (DSI studio) [https://dsi-studio.labsolver.org] (F.-c. Yeh, 2021). Seed points were manually selected based on a balance given by input data and an optimal number of the tract by visual inspections. This approach is intended to avoid the lack of reconstruction due to anatomical variability & distortions. As such, previously described criteria were applied for selecting the number of optimal seed points (Cheng et al., 2012; Zajac, Koo, Bauer, Killiany, & Behalf Of The Alzheimer’s Disease Neuroimaging, 2017). Regions, where FA was below 0.2, were automatically removed. FA and MD maps from each patient were co-registered to the patient’s MPRAGE (1 mm isotropic) by DSI studio software. On occasion, due to gross anatomical degeneration/deformations, we manually adjusted the registration to the patient MPRAGE landmarks. These segmentation realignments were performed by experienced users familiar with neuroanatomical landmarks. Axial and radial diffusivity were also calculated as these metrics are also frequently employed for describing degeneration in neurodegenerative diseases, such as PPA (Galantucci et al., 2011; Marcotte et al., 2017; McKenna et al., 2021; Tetzloff et al., 2018). Therefore, axial, and radial diffusivity (AxD and RD respectively) were also calculated in this study.

2.5. Tractography reconstructions

Whole-brain deterministic fiber tracking algorithms (with augmented tracking strategies) were used (Yeh, Verstynen, Wang, Fernández-Miranda, & Tseng, 2013). Anisotropy thresholds, angular thresholds, and step size were automatically selected. An anatomical tractography atlas was used to map all tracts and seeding regions were placed using preestablished WM probabilistic areas (Yeh et al., 2018). Tracks with a length shorter than 3mm or longer than 500 mm were discarded. A total of 500,000 seeds were placed with a distance voxel tolerance of 16 mm. Distorted tract or false connections were automatically removed using a topology-informed pruning algorithm (Yeh, Panesar, et al., 2019).

A total of 13 tracts were assessed, divided into four main groups, using a topographical anatomical atlas embedded in the DSI software. These tracts were classified as 1) Cortico-cortical tracts, which included the body of the corpus callosum body, the fasciculus arcuatus, the frontal Aslant tract, and the inferior segment of the superior longitudinal fascicle (Figure 1A, B, C, D). 2) Cortico-subcortical tracts, which contained the thalamic anterior and superior radiation tracts as well as the cortico-striatal anterior and superior tracts (Figure 2A, B); 3) Cortical-projection group, including the corticospinal tract (Cst), the corticoreticular (Cr) and corticobulbar (Cb) tracts, (Figure 3A, B, C). 4) Cerebello-cortical projection fibers involving the dentatorubrothalamic tracts and the superior cerebellar peduncle (Figure 3D, E). These tracts were selected given that they are affected in PAOS either anatomically using neuroimaging or histologically on autopsy evaluation (Keith Josephs et al., 2021; Keith Josephs et al., 2013; Keith Josephs et al., 2012; Rene Utianski et al., 2018). For all bilateral tracts, DTI analysis and tractography reconstructions were calculated on the right (R) and left (L) sides of the brain.

Figure 1 -.

Figure 1 -

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Representation of the different analyzed subsets of cortico-cortical tracts from patients with phonetic and prosodic PAOS. 3A - Body of the Corpus Callosum, 3B - Arcuatus Fasciculus, 3C - Frontal Aslant Tract, and 3D - Inferior segment of the superior longitudinal fascicle (SLF).

Figure 2 -.

Figure 2 -

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Four subsets of cortico-subcortical fibers used for the analysis of PAOS variants: 4A - Cortico-striatal anterior portion, 4B - Superior position of the Thalamic radiations. Note that the cortical components of both tracts receive from the striatum (brown) and project fibers from the thalamus (red, pink) not only to the frontal lobes (green) but also to the parietal lobes (blue).

Figure 3 -.

Figure 3 -

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Display of cortical and cerebellar cortical projection for FA and MD calculations across different PAOS subtypes. Cortical projection fibers (upper): 5A - Corticospinal fibers; 5B - Corticobulbar fiber; 5C - Corticoreticular fibers. Cerebella- cortical projections fibers (lower) 5D - Dentatorubrothalamic tract, and 5E, Superior Cerebellar Peduncle.

2.6. Speech and language network tractography reconstructions

In addition to assessing specific white matter tracts, connectivity between regions that are commonly considered to be part of the language network was assessed to summarize degeneration in this network. The patient MPRAGE images were normalized to the Mayo Clinic Adult Lifespan Template (MCALT 1.4: https://www.nitrc.org/projects/mcalt/) (Schwarz et al., 2017), and these normalization parameters were used to transform the MPRAGE-space FA and MD maps into template space. All processes were done using DSI studio software. Different GM regions have been considered as part of the language network, This has been described by dorsal and ventral pathways connecting prefrontal and temporal language-relevant regions (Friederici & Gierhan, 2013). Whereas areas from the frontal gyrus have been characterized during phonological retrieval (Rivas-Fernández, Varela-López, Cid-Fernández, & Galdo-Álvarez, 2021) others have been associated with language modulation (Kim et al., 2022; Seghier, 2013). Based on these previous findings, we determined the micro and macrostructural state of the language network including five left hemisphere regions of interest (ROIs) were selected; 1) the left inferior triangular part of the frontal gyrus (Broca’s area), 2) the left rolandic operculum region, 3) the left superior temporal gyri, 4) left supramarginal gyri; and 5) left angular gyri. Tractography reconstruction settings were kept as previously described choosing an end-to-end ROI reconstruction approach (Figure 4).

Figure 4 -.

Figure 4 -

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Comparative changes in DTI microstructure between control and PAOS language networks. 1A - From the control group (right-handed dominant), diverse views from the grey matter areas included language. Tractography reconstructions can seem like part of the fibers that constitute the network of fibers connecting different cortical language areas. 1B - DTI tractography reconstructions show a large difference in disorganization in both PAOS subtypes compared to controls. 1C - Statistically significant changes in language network microstructure can be seen in fractional anisotropy (FA) and mean diffusivity (MD) in both PAOS variants. Abbreviations: Frontal_Inf_Tri_L, left inferior frontal gyrus, triangular part.; Rolandic_Oper_L, left Rolandic operculum region; Temporal_Sup_L, left superior temporal region; SupraMarginal_L; left supramarginal gyri; Angular_L, left angular gyri.

2.7. Statistical analysis

All statistical analyses were performed using R statistical software (Team, 2021) version 4.1.2 and GraphPad Prism 9.0 for Windows (San Diego, CA, USA, www.graphpad.com). To determine whether there were group-wise differences across tracts and hemispheres, we used a Bayesian hierarchical linear model to determine whether there were differences in DTI measures across PAOS subtypes. Bayesian hierarchical linear models prospectively manage the problem of multiple comparisons and allow us to include multiple measurements per person in a single model (Greenland, 2000). These models were used for the analysis of individual tracts and hemispheres in each DTI contrast, FA or MD, predicting FA or MD respectively, using the following terms: (i) tract-and-side specific estimates for each group, (ii) an overall age term common across tracts and PAOS subtypes, and (iii) overall intercept per person (this adjusts for a person being generally “high” or generally “low” overall). After the model was fit using Markov-Chain Monte Carlo simulation, posterior probabilities were calculated from the set of posterior samples (a common method of model fitting in the Bayesian paradigm). Posterior probabilities > 0.90 were considered moderate evidence of a difference. Posterior probabilities > 0.99 (1/100 chance of being wrong) were taken as strong evidence.

Association between DTI parameters (FA &MD) and clinical variables were calculated using the Spearman rank correlation coefficient (values from 0.20 to 0.39 were considered a weak correlation, 0.40 to 0.69 a moderate correlation, and 0.70 to 0.89 a strong correlation) (Schober, Boer, & Schwarte, 2018). To facilitate the comparisons during the preprocessing, any negative correlations between DTI parameters and clinical variables were transformed into positive correlations by multiplying the variables by −1. Spearman rank correlations were also used to investigate tract-specific relationships with age. Statistical analysis to compare FA and MD from the language network between controls and PAOS variants was calculated by Kruskal-Wallis and Dunns’ multiple comparison tests.

3. Results

3.1. Demographics

The demographic and clinical features of the cohort are shown in Table 1. No differences in sex or handedness were observed across the PAOS and control groups. However, age at visit did differ across groups, with PAOS_pr older than PAOS_ph and controls. Performance on the MoCA differed across the three groups, with the greatest cognitive impairment observed in PAOS_ph. The PAOS subtypes did not differ in disease duration or total ASRS scores (i.e., apraxia of speech severity), although differences were observed in the proportion of patients with aphasia and the WAB-AQ, with the greatest proportion of aphasia presence and worse severity of aphasia in PAOS_ph. The AES total error score was also greater in PAOS_ph, as expected. Differences between PAOS_ph and PAOS_pr on the ASRS Phonetic and Prosodic subscores were significant (p< 0.006, p<0.0074 respectively). (See Table 1).

Table 1:

Summary of cohorts’ demographics. Abbreviations; paos_ph, a phonetic subtype of apraxia of speech; paos_pr, the prosodic subtype of progressive apraxia of speech; MoCA, Montreal cognitive assessment; AOS, apraxia of speech; AQ, quartile

CONTROL (N=19) PAOS_PH (N=20) PAOS_PR (N=12) P-VALUE
Sex, female 15 (78.9%) 9 (45.0%) 8 (66.7%) 0.095*
Age at visit 63 (59, 69) 66 (58, 73) 74 (70, 78) 0.015 *
Handedness (Left/Right) 0 (0%)/14 (100%) 1 (6%)/17 (94%) 2 (17%)/10 (83%) 0.348*
Aphasia present 0 (0%) 16 (80%) 5 (42%) < 0.001 *
MoCA 27 (25, 28) 21 (17, 24) 26 (23, 27) < 0.001 *
Disease duration NA 4 (3, 5) 5 (3, 5) 0.879 #
WAB-AQ NA 91 (77, 96) 98 (94, 99) <0.0032 #
MDS-UPDRS III NA 11 (8,21) 13 (7,32) 0.368 #
FAB NA 15 (9,17) 15 (13,18) 0.546 #
ASRS Total v3 NA 22 (18, 30) 20 (16, 23) 0.493 #
ASRS Phonetic subscore NA 9 (7, 12) 6 (4, 6) <0.0006 #
ASRS Prosodic subscore NA 6 (3, 8) 10 (7, 11) <0.0074 #
AES Total % error 0 (0, 2) 52 (29, 68) 13 (10, 29) < 0.001 *
Subjects with dysarthria NA 7/22 (32%) 3/12 (25%) 0.56
Dysarthria severity NA 0.43 (0.0, 1.0) 0.25 (0.0, 0.75) 0.72
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Footnote: PAOS (progressive apraxia of speech) patients are divided into two subtypes; 1) paos_ph, a phonetic subtype of apraxia of speech, and 2) paos_pr, the prosodic subtype of progressive apraxia of speech.

Abbreviations; MoCA, Montreal cognitive assessment; MoCA, Montreal cognitive assessment; WAB-AQ, Western Aphasia Battery - aphasia quotient; FAB, frontal assessment battery; ASRS, apraxia of speech rating scale; AES total error; NS, non-significant.

*

p-value ( Control vs PAOS_ph vs PAOS_pr) calculated by Kruskal–Wallis test

#

p value (PAOS_ph vs PAOS_pr) calculated by Mann-Whitney test.

3.2. Diffusion tensor imaging and tractography reconstructions

3.2.1. Cortico-cortical tracts

The PAOS_ph and PAOS_pr groups showed strong evidence for reduced FA and increased MD throughout all the cortico-cortical tracts compared to controls, including the body of the corpus callosum, arcuate fasciculus, frontal aslant tract, and the inferior segment of the superior longitudinal fasciculus (Figure 5, Table 2). There was slightly more degeneration in the left hemisphere across tracts (Table 2). There was strong evidence for a difference in FA in the body of the corpus callosum between subtypes, with lower FA in PAOS_pr, and moderate evidence for a difference in MD in the inferior segment of the left superior longitudinal fasciculus (SLF), with greater MD in PAOS_ph. Abnormalities in both groups were also observed in AxD and RD, although differences were more pronounced with RD. Specifically, no significant AxD changes were observed between PAOS subgroups compared to the control. However, a larger increase in AD from the PAOS_pr >PAOS_ph groups was observed in AF_R and FAT_R (Supplementary Table 1).

Figure 5 -.

Figure 5 -

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Bayesian Hierarchical linear models plot representing differences from the control mean to each group, (control vs. paos_ph and paos_pr) as well as intergroup differences. 2A - Fractional Anisotropy (FA) was compared across each group. 2B – Mean diffusivity (MD) was compared.

Table 2.

(1 of 2) - DTI fractional anisotropy (FA) and mean diffusivity (MD) of (A) Cortico-cortical and (B) Cortico-subcortical fiber populations from each PAOS group. (2 of 2) - DTI fractional anisotropy (FA) and mean diffusivity (MD) from (C) Cortical projections, and (D) Cerebello- cortical fibers from each PAOS group. Abbreviations: CC_body; body of the corpus callosum; AF, arcuatus fascicle; fat, frontal aslant tract; inner-SLF, inferior segment of the superior longitudinal fasciculus; Cstr ant; anterior portion of the cortical-striatal tract; Cstr post; posterior portion of the corticostriatal tract; That_ ant; thalamic anterior radiations; Thal_post; posterior portion of thalamic radiations; Cst, cortico-spinal tract; Cret, cortico-reticulate tract; Cb, cortical-bulbar tract; DRTT, dentato-rubro-thalamic tract; SCP, superior cerebellar peduncle.

FA A) CORTICO- CORTICAL TRACTS
CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R
control 0.459529 0.429655 0.444167 0.379754 0.370526 0.404135 0.378635
PAOS_ph 0.400300 0.383324 0.401308 0.320403 0.322368 0.356156 0.347227
PAOS_pr 0.380221 0.382473 0.395727 0.317892 0.318191 0.352841 0.339954
control vs PAOS_ph * −12.9% −10.8% −9.6% −15.6% −13.0% −11.9% −8.3%
control vs PAOS_pr * −17.3% −11.0% −10.9% −16.3% −14.1% −12.7% −10.2%
                 
MD CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R
control 0.000872 0.000750 0.000740 0.000832 0.000790 0.000754 0.000778
PAOS_ph 0.001100 0.000859 0.000824 0.001028 0.000960 0.000867 0.000851
PAOS_pr 0.001028 0.000843 0.000833 0.000988 0.000927 0.000841 0.000850
control vs PAOS_ph * 26.1% 14.6% 11.4% 23.6% 21.5% 15.0% 9.3%
control vs PAOS_pr * 17.9% 12.4% 12.6% 18.7% 17.4% 11.6% 9.2%
FA B)CORTICO- SUBCORTICAL TRACTS
Thal_ant_L Thal_ant_R Thal_sup_L Thal_sup_R Cstr_ant_L Cstr_ant_R Cstr_sup_L Cstr_sup_R
control 0.379754 0.370526 0.423155 0.426075 0.341409 0.348190 0.396855 0.415758
PAOS_ph 0.320403 0.322368 0.394482 0.390301 0.290889 0.292684 0.354476 0.386618
PAOS_pr 0.317892 0.318191 0.386277 0.396593 0.308571 0.310783 0.353312 0.371873
control vs PAOS_ph * −15.6% −13.0% −6.8% −8.4% −14.8% −15.9% −10.7% −7.0%
control vs PAOS_pr * −16.3% −14.1% −8.7% −6.9% −9.6% −10.7% −11.0% −10.6%
                   
MD Thal_ant_L Thal_ant_R Thal_sup_L Thal_sup_R Cstr_ant_L Cstr_ant_R Cstr_sup_L Cstr_sup_R
control 0.000912 0.000841 0.000841 0.000821 0.000868 0.000902 0.000816 0.000780
PAOS_ph 0.001196 0.001073 0.000881 0.000962 0.001055 0.001031 0.000881 0.000962
PAOS_pr 0.001144 0.001047 0.000993 0.000983 0.000979 0.000999 0.000956 0.000879
control vs PAOS_ph * 31.2% 27.6% 4.8% 17.1% 21.5% 14.3% 8.0% 23.3%
control vs PAOS_pr * 25.5% 24.5% 18.1% 19.8% 12.7% 10.8% 17.1% 12.6%
FA C) CORTICAL PROJECTIONS
Cst_L Cst_R Cret_L Cret_R Cb_L Cb_R
control 0.513218 0.519575 0.439589 0.428382 0.505980 0.486759
PAOS_ph 0.488020 0.503705 0.393639 0.395273 0.474679 0.452484
PAOS_pr 0.475640 0.496680 0.397461 0.398485 0.470098 0.460556
control vs paos_ph * −4.9% −3.1% −10.5% −7.7% −6.2% −7.0%
control vs paos_pr * −7.3% −4.4% −9.6% −7.0% −7.1% −5.4%
MD
Cst_L Cst_R Cret_L Cret_R Cb_L Cb_R
control 0.000777 0.000771 0.000839 0.000852 0.000742 0.000749
PAOS_ph 0.000848 0.000848 0.000997 0.000975 0.000825 0.000820
PAOS_pr 0.000879 0.000841 0.000953 0.000925 0.000820 0.000821
control vs PAOS_ph * 9.1% 10.0% 18.8% 14.4% 11.3% 9.5%
control vs PAOS_pr * 13.1% 9.1% 13.5% 8.5% 10.6% 9.5%
FA D) CEREBELLO - CORTICAL PROJECTIONS
DRTT_L DRTT_R SCP
control 0.432770 0.447115 0.480541
PAOS_ph 0.410691 0.427947 0.467143
PAOS_pr 0.406386 0.424064 0.415850
control vs PAOS_ph * −5.1% −4.3% −2.8%
control vs PAOS_pr * −6.1% −5.2% −13.5%
         
MD DRTT_L DRTT_R SCP
control 0.000790 0.000822 0.000992
PAOS_ph 0.000869 0.000881 0.001042
PAOS_pr 0.000904 0.000910 0.001207
control vs PAOS_ph * 9.9% 7.2% 5.0%
control vs PAOS_pr * 14.3% 10.7% 21.7%
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Footnote: Description of fractional anisotropy (FA) & mean diffusivity (MD) values on each cohort.

*

calculated % of changes between control and PAOS phonetic (PAOS_ph) and prosodic (PAOS_pr) groups along cortical projection and cerebello-cortical projection tracts.

3.2.2. Cortico-subcortical tracts

The PAOS_ph and PAOS_pr groups showed strong evidence for reduced FA and increased MD throughout all cortico-subcortical tracts compared to controls, including the anterior and superior thalamic radiations and corticostriatal tracts (Figure 5, Table 2). There was strong evidence for greater MD in the left superior thalamic radiation and left superior corticostriatal tract in PAOS_pr compared to PAOS_ph, and strong evidence for greater MD in the superior corticostriatal tract in PAOS_ph compared to PAOS_pr. AxD as well as RD from the thalamic superior radiation (Thal_sup) and corticostriatal superior tracts (Cstr_Sup) was largely increased in the PAOS_ph group (Supplementary Table 1).

3.2.3. Cortical projections

The PAOS_ph and PAOS_pr groups showed strong or moderate evidence for reduced FA and increased MD throughout most of the cortical projection tracts compared to controls, including the corticospinal, corticoreticular, and corticobulbar tracts. These findings tended to be stronger in the left hemisphere for both PAOS subtypes (Table 2). There was no evidence for differences between PAOS_ph and PAOS_pr in any of the cortical projection tracts, nor large differences in AxD or RD along these tracts (Supplementary Table 1).

3.2.4. Cerebello-cortical projections

The PAOS_ph and PAOS_pr groups showed strong evidence for reduced FA and increased MD in the dentatorubrothalamic tract compared to controls, with no evidence for differences between subtypes. However, degeneration of the superior cerebellar peduncle was greater in PAOS_pr compared to PAOS_ph for both FA and MD (Table 2), with strong evidence for greater MD and moderate evidence for lower FA in PAOS_pr compared to PAOS_ph. It is worth noticing that RD obtained from the superior cerebellar peduncle (SCP) was comparatively more elevated in the PAOS_pr group (Supplementary Table 1).

3.2.5. Speech & language network

On tractography reconstructions, large disorganization in the language network was seen in both PAOS subtypes, compared to similar network nodes in the controls. Decreased FA and increased MD were observed in the language network in both PAOS_ph and PAOS_pr compared to controls, with no differences observed between the PAOS subtypes (Figure 4).

3.3. Correlative clinical-imaging findings

Spearman rank order correlations were used to assess associations between DTI parameters (FA, MD) and different clinical scores within each subtype (Table 3). In PAOS_ph, increased scores on the MDS-UPDRS III were moderately correlated with reduced FA in the body of the corpus callosum, right inferior segment of the SLF, and right superior thalamic radiation. The AES showed a moderate correlation with increased MD in the right corticostriatal tract in PAOS_ph. In PAOS_pr, the ASRS total score was strongly correlated with FA in the right frontal aslant tract, and moderately correlated with the body of the corpus callosum, left frontal aslant tract, right inferior segment of the SLF, left corticobulbar tract, and left and right corticoreticular tracts. Similar findings were observed with MD. Furthermore, in PAOS_pr, the AES showed a strong correlation with FA and MD in the right inferior segment of the SLF, as well as with FA in the right frontal aslant tract and MD in the right dentatorubrothalamic tract. Moderate correlations were observed between the AES and corticoreticular tracts, left frontal aslant tract, left dentatorubrothalamic tracts, and the inferior segment of the SLF.

Table 3 –

Multiple correlative analyses between fractional anisotropy (FA) (1 of 2) and mean diffusivity (MD) with clinical scores (2 of 2). Abbreviations: MDS- UPDRS III, the motor portion of the Movement Disorder Society - Unified Parkinson Disease Rating Scale. WAB-AQ, Western Aphasia Battery - aphasia quotient (AQ); ASRS Total v3, the third version of the Apraxia of Speech Rating Scale; AES_ttl_pct_err, articulatory total percentage of error score.

PAOS_ph (FA)
Spearman (r) A) Cortico-cortical circuitry B) Cortico-subcortical circuitry C) Cortical projection circuitry D) Cerebello -cortical circuitry
CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R Thal sup_L Thal_sup_R Cstr sup_L Cstr sup_R Cst_L Cst_R Cb_L Cb_R Cret_R Cret_R DRTT_L DRTT_R SCP
MoCA 0.25 0.25 0.19 0.39 0.08 −0.01 0.12 0.07 0.16 0.09 −0.01 −0.26 −0.45 −0.11 0.02 0.13 −0.02 −0.26 −0.42 −0.22
FAB 0.17 0.41 0.35 0.63 0.34 0.21 0.35 0.17 0.45 0.63 0.31 0.21 0.05 0.08 0.26 0.42 0.30 0.30 0.15 0.25
MSD - UPDRS III 0.53 −0.36 −0.37 0.43 0.34 0.31 0.51 0.44 0.54 0.45 0.44 0.23 0.32 0.10 0.27 0.19 0.20 0.48 0.41 0.35
WAB AQ 0.09 0.33 0.13 0.19 0.19 0.22 0.08 0.03 0.16 0.12 0.04 0.07 −0.34 0.19 0.08 0.24 0.29 0.02 −0.16 −0.13
ASRS_v3 Total 0.16 −0.20 −0.15 0.01 0.43 0.05 0.04 −0.05 0.09 0.10 −0.05 0.07 −0.11 0.01 −0.02 0.07 0.13 0.11 0.06 −0.03
AES_ttl_pct_err 0.24 −0.36 −0.20 0.27 0.15 0.26 0.17 0.15 0.31 0.18 0.16 0.11 −0.07 0.15 0.16 0.01 0.03 0.11 0.02 −0.16
Disease duration −0.24 0.26 0.35 −0.34 0.14 −0.24 −0.43 0.10 −0.23 −0.48 −0.45 −0.29 −0.38 −0.21 −0.40 −0.14 −0.01 −0.19 −0.17 −0.20
PAOS_pr (FA)
Spearman (r) A) Cortico-cortical circuitry B) Cortico-subcortical circuitry C) Cortical projection circuitry D) Cerebello -cortical circuitry
CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R Thal sup_L Thal_sup_R Cstr sup_L Cstr sup_R Cst_L Cst_R Cb_L Cb_R Cret_L Cret_R DRTT_L DRTT_R SCP
MoCA −0.49 −0.68 −0.24 −0.21 −0.19 −0.61 −0.12 −0.58 −0.46 −0.66 −0.72 −0.42 −0.50 −0.17 −0.21 −0.05 −0.42 −0.77 −0.51 −0.03
FAB −0.27 −0.34 0.02 −0.03 0.02 −0.31 0.12 −0.39 −0.29 −0.47 −0.41 −0.24 −0.21 −0.02 −0.05 0.18 −0.02 −0.42 −0.28 0.14
MSD - UPDRS III 0.03 0.03 0.11 0.02 −0.16 −0.37 −0.02 0.1 0.25 −0.16 −0.19 −0.43 −0.09 0 −0.04 −0.04 0.05 −0.41 −0.33 0.07
WAB AQ −0.19 −0.51 −0.25 −0.18 0.11 −0.56 −0.12 −0.52 −0.34 −0.33 −0.41 −0.52 −0.61 −0.53 −0.58 −0.05 −0.25 −0.74 −0.33 0.18
ASRS_v3 Total 0.56 −0.17 −0.65 0.64 0.81 0.07 0.55 0.4 0.42 0.14 0.21 0.4 0.48 0.5 0.32 0.6 0.53 0.13 0.39 0.29
AES_ttl_pct_err 0.45 −0.26 −0.70 0.55 0.72 0.07 0.75 0.4 0.36 0.11 0.17 0.22 0.48 0.36 0.28 0.65 0.57 0.23 0.47 0.51
Disease duration 0.28 −0.30 −0.36 0.29 0.33 0.1 0.54 0.34 0.38 −0.1 0 0.39 0.66 0.77 0.73 0.39 0.44 0.37 0.39 0.4
PAOS_ph (MD)
Spearman (R) A) Cortico-cortical circuitry B) Cortico-subcortical circuitry C) Cortical projection circuitry D) Cerebello -cortical circuitry
CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R Thal sup_L Thal_sup_R Cstr sup_L Cstr sup_R Cst_L Cst_R Cb_L Cb_R Cret_R Cret_R DRTT_L DRTT_R SCP
MoCA −0.23 −0.41 −0.30 −0.39 −0.19 −0.14 −0.24 −0.12 −0.29 −0.12 −0.29 −0.12 −0.12 −0.42 −0.09 −0.12 −0.06 −0.12 −0.09 −0.09
FAB −0.46 −0.56 −0.25 −0.44 −0.28 −0.23 −0.33 −0.22 −0.36 −0.22 −0.36 −0.42 −0.42 −0.32 −0.26 −0.06 −0.24 −0.48 −0.44 −0.32
MSD - UPDRS III 0.35 0.36 0.23 0.36 0.21 0.36 0.43 0.45 0.46 0.45 0.31 0.24 0.24 0.13 0.13 0.19 0.26 0.17 0.32 0.1
WAB AQ 0.26 −0.48 −0.30 0.40 0.28 0.17 0.19 0.19 0.21 0.19 0.36 0.23 0.24 0.19 0.00 0.20 0.22 0.23 −0.07 0.09
ASRS_v3 Total 0.15 −0.04 0.14 0.19 0.27 0.17 0.14 0.22 0.25 0.22 0.32 0.20 0.21 0.08 −0.04 −0.06 −0.07 0.05 −0.02 −0.14
AES_ttl_pct_err 0.21 0.28 0.25 0.35 0.22 0.45 0.26 0.32 −0.02 0.32 0.59 0.33 0.33 0.27 0.08 −0.06 0.08 0.11 0.04 −0.37
Disease duration −0.30 −0.39 −0.32 −0.24 −0.38 −0.14 −0.39 −0.31 −0.13 −0.31 −0.04 −0.23 −0.23 −0.30 −0.48 0.02 −0.13 −0.17 −0.08 −0.05
PAOS_pr (MD)
Spearman (r) A) Cortico-cortical circuitry B) Cortico-subcortical circuitry C) Cortical projection circuitry D) Cerebello -cortical circuitry
CC_body AF_L AF_R FAT_L FAT_R SLF_L SLF_R Thal sup_L Thal_sup_R Cstr sup_L Cstr sup_R Cst_L Cst_R Cb_L Cb_R Cret_R Cret_R DRTT_L DRTT_R SCP
MoCA 0.25 0.76 0.52 0.21 0.28 0.73 0.20 0.34 0.34 0.50 0.59 0.52 0.55 0.49 0.46 −0.24 0.12 0.32 0.17 −0.63
FAB 0.07 0.46 0.16 0.00 0.10 0.51 0.20 0.17 0.07 0.16 0.21 0.29 0.14 0.34 0.10 −0.23 0.03 −0.01 −0.24 −0.89
MSD - UPDRS III 0.17 −0.11 −0.39 0.02 −0.08 −0.15 −0.02 0.10 0.25 −0.16 −0.19 0.10 0.05 −0.11 0.12 0.33 0.27 0.19 0.11 0.33
WAB AQ −0.05 0.59 0.48 −0.18 −0.28 −0.49 −0.12 0.20 −0.34 0.21 −0.41 −0.28 −0.62 −0.81 −0.57 0.24 −0.13 −0.19 −0.07 0.52
ASRS_v3 Total 0.33 0.36 0.62 0.41 0.81 0.44 0.55 0.40 0.42 0.14 0.21 0.28 0.33 0.38 0.36 0.51 0.50 0.31 0.57 0.14
AES_ttl_pct_err 0.32 0.28 0.59 0.39 0.64 0.38 0.75 0.40 0.36 0.11 0.17 0.20 0.40 0.26 0.41 0.55 0.53 0.51 0.74 0.45
Disease duration 0.21 0.32 0.40 0.20 0.58 0.27 0.54 0.34 0.38 −0.10 0.00 0.08 0.64 0.69 0.64 0.33 0.58 0.36 0.44 0.05
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Footnote: Correlative analysis between FA & MD vs clinical scores in PAOS phonetic and prosodic groups. Note that, to facilitate the comparisons, during the preprocessing, any negative correlations between DTI parameters and clinical variables were transformed into positive correlations by multiplying the variables by −1.

Abbreviations: CC_body; the body of the corpus callosum; AF, arcuatus fascicle; fat, frontal aslant tract; SLF, the inferior segment of the superior longitudinal fasciculus; Cstr ant; anterior portion of the cortical-striatal tract; Cstr post; posterior portion of the corticostriatal tract; That_ ant; thalamic anterior radiations; Thal_post; posterior portion of thalamic radiations; Cst, cortico-spinal tract; Cret, cortico-reticulate tract; Cb, cortical-bulbar tract; DRTT, dentatorubro-thalamic tract; SCP, superior cerebellar peduncle.

The WAB-AQ showed moderate correlations in the expected direction with MD in a few tracts, including the left arcuate fasciculus in PAOS_ph and the left inferior segment of the SLF, as well as the corticospinal and corticobulbar tracts in PAOS_pr. MoCA showed a moderate correlation with FA and MD from the left frontal aslant tract (FAT) and FAB was strongly correlated with FA from left FAT and MD with left arcuatus fasciculi (AF) (Table 3). We observed very few relationships between our tract metrics and age, with only a few moderate correlations (Supplemental Table 2).

4. Discussion

This study shows that diffusion tractography measures were able to differentiate microstructural damage in different PAOS subtypes. Both PAOS subtypes showed widespread degeneration throughout the language, cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical tracts, although PAOS_pr showed greater degeneration of the body of the corpus callosum, superior thalamic radiations, and superior cerebellar peduncle, while PAOS_ph showed greater degeneration of inferior segment of the SLF. Significant correlations were identified between tract metrics and clinical scores suggesting that degeneration of these white matter networks is related to clinical phenotype and severity in PAOS.

PAOS represents a unique, “higher level” motor speech disorder, occasionally manifesting without other speech or language deficits likely due to an alteration in connectivity patterns between different cortical areas. In particular, in our study, we found overall larger FA and MD changes in the left cortical tracts, in a predominantly right-handed cohort, which may partly reflect the fact that a large proportion of the cohort had aphasia. However, is worth noticing that the left hemisphere is also dominant for motor speech planning/programming and 20% (PAOS_ph) and 58% (PAOS_pr) were not aphasic. Both PAOS groups also showed abnormalities in the language network as evidenced by intrinsic macro (tractography finding) and microstructural irregularities (DTI measurements) in the WM areas linking cortical areas related to language functions. Many other similarities were observed between the PAOS subtypes, with both subtypes showing widespread involvement of cortico-cortical, cortico-subcortical, cortical-projection, and cerebello-cortical tracts suggesting potential involvement of these tracts in PAOS. These similarities are not surprising given that the majority of PAOS patients have both phonetic and prosodic speech characteristics, albeit with one predominating over the other.

The body of the corpus callosum was affected in both subtypes but showed greater degeneration in PAOS_pr. Impaired prosody in speech has indeed been associated with vascular, neurodegenerative, or malformative lesions predominantly involving this structure (Paul, Van Lancker-Sidtis, Schieffer, Dietrich, & Brown, 2003; Wright, Saxena, Sheppard, & Hillis, 2018). More specifically, recent studies have proposed an interplay between the speech processing streams in the left and right hemispheres via the posterior portion of the corpus callosum, building a structural basis for the coordination and integration of local prosodic features during speech comprehension (Sammler, Kotz, Eckstein, Ott, & Friederici, 2010). Degeneration of the body of the corpus callosum may also imply the involvement of neighboring cortical areas, such as the supplementary motor area, in the PAOS_pr subtype. The other cortico-cortical tract that showed differences between PAOS subtypes was the inferior segment of the SLF, with greater involvement in PAOS_ph. The inferior segment of the SLF is an association tract that connects the posterior frontal and parietal regions of the brain. Greater involvement of this tract in PAOS_ph concurs with our previous autopsy and imaging findings suggesting greater neocortical involvement in PAOS_ph compared to PAOS_pr (Josephs et al., 2021; R. L. Utianski et al., 2018). In that study, patients with PAOS_ph most commonly had underlying corticobasal degeneration pathology, which targets the posterior frontal and parietal lobes (Josephs et al., 2021).

The arcuate fasciculus and frontal aslant tracts were affected to a similar degree in both PAOS subtypes. The arcuate fasciculus has previously been associated with apraxia of speech severity in patients with PAOS (Chenausky, Paquette, Norton, & Schlaug, 2020), and studies using voxel‐based lesion-symptom mapping associated apraxia of speech with damage to this tract. We observed some relationships between this tract and the severity of aphasia which is consistent with the fact that the arcuate fasciculus projects from Broca’s area. Degeneration of the frontal aslant tract has also been demonstrated in PAOS (Valls Carbo et al., 2022) and has been associated with verbal fluency in patients with agrammatic aphasia (Catani et al., 2013). The frontal aslant tract plays a domain-general role in the planning, timing, and coordination of bilateral sequential motor movements. While the left frontal aslant tract has a putative role in supporting speech and language function (with a particular focus on speech initiation and verbal fluency), the right frontal aslant tract supports executive function, namely inhibitory control and conflict monitoring for action (Dick, Garic, Graziano, & Tremblay, 2019). Results from our study show bilateral structural impairment of the frontal aslant tract, independently of the PAOS subtype, which may imply a common brain circuit mechanism across subtypes. In our previous tractography study, we found that degeneration of the frontal aslant tract was particularly associated with the presence and severity of aphasia in PAOS. In contrast, in the current study, we observed correlations between apraxia of speech severity and integrity of the frontal aslant tract, particularly on the right, although only in PAOS_pr. The reason for this discrepancy is unclear, although it may suggest a role of the frontal aslant tract in both agrammatic aphasia and prosodic apraxia of speech; perhaps dependent upon which segments of the tract is affected (Valls Carbo et al., 2022). As such, these differences may reflect the different distribution of severity of aphasia and/or AOS, or AOS subtype. It is also possible that these results reflect the interconnected nature of all systems of tracts (and not with a unique focus on the FAT) or a broader bilateral role of this circuitry in PAOS_pr.

The cortico-subcortical tracts, including the thalamic radiations and corticostriatal tracts, were affected in both PAOS subtypes. The cortico-subcortical neural network which includes both basal ganglia and the thalamus may participate in the monitoring of planning, coordination, timing, sequencing, and selection of the proper motor programs during the implementation of complementary stages of verbal production (Silveri, 2021), and the right hemisphere might be attributed a role in the emotional-affective components of prosody (Weintraub, Mesulam, & Kramer, 1981). Results from our study show that both PAOS_ph and PAOS_pr showed damage on both sides of the brain. There was strong evidence that the left superior thalamic radiation, which connects the ventral thalamus with the sensorimotor cortex, was affected to a greater degree in PAOS_pr compared to PAOS_ph, and there was evidence that this tract may be related to the severity of parkinsonism in PAOS_ph. Indeed, patients with PAOS, particularly those with PAOS_pr (Whitwell et al., 2017), commonly evolve into an atypical Parkinsonian syndrome (Josephs et al., 2014; Seckin et al., 2020).

The cortical-projection fibers, such as the corticobulbar and corticospinal tracts, originate from the motor, premotor, and supplementary motor areas of the brain (Bhardwaj & Yadala, 2022), grey matter regions which are predominantly affected in PAOS (Keith Josephs et al., 2012). In our study, significant differences were seen in FA and MD values from all cortical-projection fibers in both PAOS subtypes compared to the control group. We also observed correlations between apraxia of speech and aphasia severity and integrity of the cortical-projection fibers in PAOS_pr patients, suggesting that degeneration of these tracts may play a role in the speech and language characteristics of these patients. These tracts are also often affected by the 4RT disorders that underlie PAOS (Josephs et al., 2006; Stejskalova et al., 2019).

We observed degeneration of the dentatorubrothalamic tract (DRTT) in both PAOS subtypes but found greater involvement of the superior cerebellar peduncle (SCP) in PAOS_pr compared to PAOS_ph patients. As such, studies quantifying spontaneous speech performance identified the potential involvement of DRTT in patients who underwent subthalamic deep nucleus deep brain stimulation (DBS) in patients with Parkinson’s disease (PD). More specifically, overall spontaneous speech and fluency were affected more negatively in patients with akinetic-rigid PD than in those with tremor-dominant PD when there was DRTT involvement (Fenoy, McHenry, & Schiess, 2017). This also concurs with our previous study in which we observed the involvement of the SCP only in PAOS_pr patients (Utianski et al., 2018). We hypothesize that involvement of this structure is related to the fact that PAOS_pr patients are most likely to have PSP pathology (Josephs et al., 2021), with SCP degeneration being a characteristic feature of this disorder (Gatto et al., 2022; Paviour, Price, Stevens, Lees, & Fox, 2005). When the whole DRTT was assessed, degeneration was observed in both subtypes, likely because superior aspects are being affected in the PAOS_ph patients. On the other hand, it is also possible that the DRTT has no role in PAOS or aphasia, and these associations are confounded by disease severity.

There were significant age differences between the PAOS subtypes, and age differences were accounted for by the Bayesian model. Furthermore, the Bayesian models suggested that age was not a relevant confounding factor, and we observed few relationships between our tract metrics and age when assessed directly. Importantly, no significant differences between groups were seen in disease duration, which may imply a true disparity due to microstructural differences captured by DTI characteristics across different WM tracks. Differences in associations between DTI and clinical scores among PAOS variants may also be related to disparities in group size. Furthermore, it is difficult to disentangle differential relationships between tract metrics and apraxia of speech versus aphasia since many patients in the cohort had both clinical features, and both will likely increase in severity with disease duration. Future studies that assess patients with PAOS that do not have aphasia, or patients with agrammatic aphasia without apraxia of speech, will be needed to validate how tract metrics specifically relate to these two clinical symptoms and to help validate these symptoms/syndromes as neurobiologically distinct. It should be noted that previously published cohorts of patients with agrammatic primary progressive aphasia (PPA) commonly also have apraxia of speech, and hence PAOS and PPA are somewhat overlapping diagnostic terms. However, PAOS includes patients with apraxia of speech without any evidence of aphasia or language impairment, while PPA requires evidence of aphasia/language impairment (Mesulam, 1982, 2001). Dysarthria severity did not differ between groups, so it is unlikely that dysarthria is confounding our group findings. Moreover, DTI-based tractography is inherently limited to reproducing crossing fibers and other complex WM architectures. Therefore, alternative diffusion models able to overcome this limitation are needed (Gatto et al., 2018; Kamiya, Hori, & Aoki, 2020). Ultimately, our study does not address patients with an equal predominance of phonetic and prosodic characteristics (mixed type) due to the low number of patients with this categorization.

5. Conclusions

Our findings demonstrate that a widespread network of WM tracts is involved in PAOS and that the degree of involvement of specific tracts differs according to the PAOS subtype. These differences may shed light on the neuroanatomical underpinnings of specific speech characteristics in apraxia of speech, as well as reflect different neuropathological underpinnings of PAOS subtypes. The outcomes of this study increase understanding of PAOS connectivity deficits and demonstrate the differences of DTI tractography that offer neuroimaging support of the clinically defined PAOS subtypes.

Supplementary Material

1

Supplementary Table 1- Descriptive changes in axial and radial diffusivity along four groups of white matter tracts.

Supplementary Table 2 - Spearman correlation between age at MRI vs. FA and MD across all tracks from PAOS_ph and PAOS_ pr.

Funding

This work was funded by the National Institute of Health (NIH) under grants number # NIH: R01-DC12519 and R01-DC14942.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declaration of competing interest

The authors declare that they have no conflict of interest. All procedures performed in this study were approved by and conducted following the ethical standards of the institutional human research ethics committee. In doing so, they were performed according to the standards of the 1964 Helsinki Declaration and its later amendments or comparable standards. Informed consent was obtained from all individual participants included in this study.

CRediT authorship contribution statement

Rodolfo Gatto: Conceptualization, Methodology, Investigation, Writing - Original draft, Software, Validation, Visualization. Peter Martin: Methodology, Formal Analysis, Software, Visualization, Writing - Review & Editing. Rene L. Utianski: Investigation, Resources, Writing, - Review & Editing. Joseph R. Daffy: Investigation, Resources, Writing, - Review & Editing. Heather M. Clark: Investigation, resource, Writing, - Review & Editing. Hugo Botha: Investigation, Resources. Mary M. Machulda: Investigation, Resources, Writing, - Review & Editing. Keith A. Josephs: Investigation, Writing, - Review & Editing, Resources, Supervision, Project Administration, Funding Acquisition. Jennifer L. Whitwell: Investigation, Writing, - Review & Editing, Resources, Supervision, Project Administration, Funding Acquisition.

Data availability

Data from this study are available in a publicly accessible repository that can be access using the following link: https://osf.io/xdteb/?view_only=b923817f54864147ba6a6901d93a6cd3. No part of the study procedures or analysis plans was preregistered prior to the research being conducted. R-codes to calculate the hierarchical linear model for this study are available in the repository.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1

Supplementary Table 1- Descriptive changes in axial and radial diffusivity along four groups of white matter tracts.

Supplementary Table 2 - Spearman correlation between age at MRI vs. FA and MD across all tracks from PAOS_ph and PAOS_ pr.

NIHMS1944020-supplement-1.docx (41.5KB, docx)

Data Availability Statement

Data from this study are available in a publicly accessible repository that can be access using the following link: https://osf.io/xdteb/?view_only=b923817f54864147ba6a6901d93a6cd3. No part of the study procedures or analysis plans was preregistered prior to the research being conducted. R-codes to calculate the hierarchical linear model for this study are available in the repository.

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