Structural disconnections associated with language impairments in chronic post-stroke aphasia using disconnectome maps

Abstract

Inconsistent findings have been reported about the impact of structural disconnections on language function in post-stroke aphasia. This study investigated patterns of structural disconnections associated with chronic language impairments using disconnectome maps.

Seventy-six individuals with post-stroke aphasia underwent a battery of language assessments and a structural MRI scan. Support-vector regression disconnectome-symptom mapping analyses were performed to examine the correlations between disconnectome maps, representing the probability of disconnection at each white matter voxel and different language scores. To further understand whether significant disconnections were primarily representing focal damage or a more extended network of seemingly preserved but disconnected areas beyond the lesion site, results were qualitatively compared to support-vector regression lesion-symptom mapping analyses.

Part of the left white matter perisylvian network was similarly disconnected in 90% of the individuals with aphasia. Surrounding this common left perisylvian disconnectome, specific structural disconnections in the left fronto-temporo-parietal network were significantly associated with aphasia severity and with lower performance in auditory comprehension, syntactic comprehension, syntactic production, repetition and naming tasks. Auditory comprehension, repetition and syntactic processing deficits were related to disconnections in areas that overlapped with and extended beyond lesion sites significant in SVR-LSM analyses. In contrast, overall language abilities as measured by aphasia severity and naming seemed to be mostly explained by focal damage at the level of the insular and central opercular cortices, given the high overlap between SVR-DSM and SVR-LSM results for these scores.

While focal damage seems to be sufficient to explain broad measures of language performance, the structural disconnections between language areas provide additional information on the neural basis of specific and persistent language impairments at the chronic stage beyond lesion volume. Leveraging routinely available clinical data, disconnectome mapping furthers our understanding of anatomical connectivity constraints that may limit the recovery of some language abilities in chronic post-stroke aphasia.

Introduction

Individuals with aphasia may present with various language impairments following an acquired brain injury such as a stroke. Multiple interdependent neurobiological events occur during and after a stroke, resulting in brain tissue death in the gray and white matter (Quillinan, Herson, & Traystman, 2016) and critical disruption of connections between brain regions responsible for language processing. Over the last decades, neuroimaging studies have provided insight into the pathophysiology of these language deficits, and the findings can be broadly categorized into two sets: 1) the effect of focal brain damage on language function, 2) the influence of network-level disruptions on language behavior (Kiran & Thompson, 2019). Here, we will first review these observations in the context of their methodology, focusing on studies using structural data, and then propose a complementary analysis of structural connectivity disruptions that can further our understanding of language impairments in post-stroke aphasia.

Voxel-based lesion-symptom mapping has revealed the relationship between cortical injury and language impairment (VLSM, Bates et al., 2003). Lesion topography is associated with a range of linguistic abilities after stroke, such as speech production and speech comprehension (Borovsky, Saygin, Bates, & Dronkers, 2007; Henseler, Regenbrecht, & Obrig, 2014; Kümmerer et al., 2013; Mirman, Chen, et al., 2015; Price, Seghier, & Leff, 2010), and more specifically verbal fluency (Baldo, Schwartz, Wilkins, & Dronkers, 2006), picture naming (Akinina et al., 2019; Døli, Helland, Helland, & Specht, 2020; Henseler et al., 2014; Piras & Marangolo, 2010), semantic processing (Halai, Woollams, & Lambon Ralph, 2017; Henseler et al., 2014; Mirman, Chen, et al., 2015; Schumacher, Halai, & Lambon Ralph, 2019; Schwartz et al., 2009; Walker et al., 2011), phonological processing (Halai et al., 2017; Ripamonti et al., 2018; Schumacher et al., 2019), repetition (Døli et al., 2020; Fridriksson et al., 2010; Henseler et al., 2014; Kümmerer et al., 2013; Ripamonti et al., 2018), syntactic processing (den Ouden et al., 2019; Dronkers, Wilkins, Van Valin, Redfern, & Jaeger, 2004; Lukic et al., 2020; Magnusdottir et al., 2013; Rogalsky et al., 2017), number and word reading (Døli et al., 2020; Piras & Marangolo, 2009) and spelling (Rapp, Purcell, Hillis, Capasso, & Miceli, 2016). Traditionally, VLSM uses t-statistics at each voxel of the brain to determine whether the degree of injury is related to the language performance (Bates et al., 2003). Lesion-symptom mapping analyses, therefore, provide information on the role of different brain areas in language performance. Nevertheless, they present inherent limitations. First, lesion coverage is heterogeneous across voxels and restricted to particular vascular territories in most studies (Karnath, Sperber, & Rorden, 2019; Rudrauf et al., 2008) which influences the statistical power at each voxel (Rudrauf et al., 2008). Consequently, the analysis may include a spatial bias toward the center of the vascular territory affected by the stroke (Karnath et al., 2019; Mah, Husain, Rees, & Nachev, 2014). Second, damage to different regions can cause the same language impairment if these regions belong to the same structural or functional network (Fridriksson et al., 2018; Karnath et al., 2019; Price, Hope, & Seghier, 2017). For these reasons, the prediction power and the interpretation of standard lesion–outcome associations are limited (Karnath et al., 2019; Kimberg, Coslett, & Schwartz, 2007; Price et al., 2017).

Furthermore, stroke damage can disrupt distant regions' structure and function by modifying their metabolism (Carrera & Tononi, 2014). Von Monakow has defined this neurobiological phenomenon as diaschisis (von Monakow, 1914). Carrera and Tononi have recently extended this notion to ‘connectomal diaschisis’ to describe remote “changes in the structural and functional connectomes, including disconnections and reorganization of subgraphs” (Carrera & Tononi, 2014, p. 2419). Hence, language deficits might arise from seemingly undamaged but disconnected regions involved in language processing (Catani & Mesulam, 2008; Price et al., 2017). Interestingly, structural disconnection measures seem to better predict functional connectivity disruption within and between large-scale networks than region-based or voxel-based damage measures (Griffis, Metcalf, Corbetta, & Shulman, 2019). Recent studies examining affected anatomical networks in stroke survivors have provided a richer understanding of the relationship between aphasia and its neural underpinnings.

Following the assumption that network-level analyses provide a more comprehensive interpretation of the relationship between clinical symptoms and physiological disruptions caused by the stroke lesion (Catani & Mesulam, 2008), several studies have examined the impact of infarcts on structural network connectivity and how white matter disruptions relate to language dysfunction. This paper focuses on methods that measure direct lesions' effect on anatomical connections (see Zhang et al., 2021 for a meta-analysis of studies using diffusion metrics to investigate white matter integrity in spared tracts). In post-stroke aphasia studies, researchers have mainly used two types of measurements based on white matter tractography data to investigate structural disconnections: (i) reduction in connection density (i.e., percentage number of fibers connected to a cortical region compared to the homologous cortical area), and (ii) a binary measure of tract discontinuity. In the first approach, Bonilha and colleagues found that reduced fiber density at two left hemisphere cortical regions (i.e., Brodmann areas 45 and 22) was associated with the degree of impairment in specific language tasks, but not with the overall aphasia severity (Bonilha, Rorden, & Fridriksson, 2014). The second approach calculates binary measures of disconnection. It considers a tract to be disconnected “if a lesion either disconnects one part of the tract from another or completely destroys one end of the tract” (Hope & Price, 2016, p. 1171). Two studies demonstrated that binary disconnection of the left arcuate fasciculus was associated with deficits in naming (Geller, Thye, & Mirman, 2019; Hope, Seghier, Prejawa, Leff, & Price, 2016). However, since this technique relies on a single value for a whole tract, it may be more sensitive to image processing errors, such as misregistration between the lesion map and the probabilistic white matter atlas needed for this method. An error of measurement at one portion of the tract would lead to the opposite category definition for the whole tract (i.e., from disconnected to spared and vice versa) (Geller et al., 2019). Mapping disconnected white matter fibers at the voxel level in the whole brain is one way to overcome this limitation. Specifically, detailing the topological distribution of structural network disruption voxel by voxel in post-stroke aphasia provides information to elucidate clinical-anatomical relationships at the level of the affected connectome, from and beyond the lesion site. It also enables one to identify and trace pathways that may be affected by the distal effects of stroke damage that are not easily measurable with actual MRI techniques (Carrera & Tononi, 2014). The most direct way to identify structural disconnections in the whole brain would be to trace fibers that cross each patient's brain's damaged area using diffusion-weighted imaging data and fiber-tracking algorithms (Basser, Mattiello, & LeBihan, 1994; Mori & van Zijl, 2002). However, when white matter tracts are directly damaged, reconstruction of the tract's remaining portions may not be possible (Auriat, Borich, Snow, Wadden, & Boyd, 2015). Here, we apply an alternative and complementary approach to examine the anatomical substrates of neural disruption in aphasia by constructing disconnectome maps (Foulon et al., 2018). By using a large reference set of high-quality tractograms from healthy controls, these disconnectome maps provide the probability of structural disconnection at each voxel without the need to acquire diffusion-weighted images (Foulon et al., 2018). Specifically, in this work, the probability of disconnection refers to the voxel-wise probability of tracking a white matter fiber in healthy controls. These fibers are then considered disconnected if they enter the infarcted area when overlaid with the patient's lesion map. This tool was first used to identify potentially disconnected tracts related to deficits in language processing, decision making, and memory in three well-studied historical patients (Thiebaut de Schotten et al., 2015). Subsequently, this technique has been used in patients following an acquired brain injury to identify disconnected networks related to the overall language behavior (Salvalaggio, De Filippo De Grazia, Zorzi, Thiebaut de Schotten, & Corbetta, 2020) or specific language impairments, such as poor fluency performance (Foulon et al., 2018) and repetitive verbal behaviors (Mandonnet et al., 2019; Torres-Prioris et al., 2019). Three of these studies included individuals who underwent a brain resection due to a brain tumor or suffered a traumatic brain injury, and each examined one language component only (Foulon et al., 2018; Mandonnet et al., 2019; Torres-Prioris et al., 2019). Using multivariate analyses, Salvalaggio et al. (2020) demonstrated that disconnectome maps can predict overall language behavior variability in stroke survivors at a similar level as lesion maps, with slightly less accuracy (i.e. 41% vs 48%). A previous study from the same group using the same method found similar results with 44% of language behavior variability explained by lesion maps (Corbetta et al., 2015). Using a similar method, Kuceyeski et al. (2015) evaluated disconnections at each cortical region and found that disconnections at medial regions predicted language scores. Notably, these studies measured language behavior either with screening tests or as a composite score obtained from multiple subtests of a language battery and, thus, did not identify disconnections related to specific language impairments in individuals with chronic post-stroke aphasia.

In the present study, we implement a comprehensive clinical-neuroanatomical investigation of the impact of white matter disconnections on a range of language abilities in a large cohort of patients with different types of chronic post-stroke aphasia. We then compare the results from disconnectome-symptom mapping (DSM) analyses to more standard lesion-symptom mapping (LSM) analyses to further distinguish between the remote pathological effects and the loss of brain tissue on chronic language deficits. We hypothesize that overall aphasia severity and specific language impairments, such as naming, repetition, syntactic processing, and auditory comprehension, will be associated with disconnections in the left perisylvian connectome due to long-range fiber pathways affected by the lesions. Further, these language impairments will be explained by disconnections that overlap and extend beyond lesion sites associated with each impairment.