The right uncinate fasciculus supports verbal short-term memory in aphasia

doi:10.21203/rs.3.rs-2060589/v1

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The right uncinate fasciculus supports verbal short-term memory in aphasia

https://doi.org/10.21203/rs.3.rs-2060589/v1

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published 02 Apr, 2023

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Verbal short-term memory (STM) deficits are associated with language processing impairments in people with aphasia. Importantly, the integrity of STM can predict word learning ability and anomia therapy gains in aphasia. While the recruitment of perilesional and contralesional homologous brain regions has been proposed as a possible mechanism for aphasia recovery, little is known about the white-matter pathways that support verbal STM in post-stroke aphasia. Here, we investigated the relationships between the language-related white matter tracts and verbal STM ability in aphasia. Nineteen participants with post-stroke chronic aphasia completed a subset of verbal STM subtests of the TALSA battery including nonword repetition (phonological STM), pointing span (lexical-semantic STM without language output) and repetition span tasks (lexical-semantic STM with language output). Using a manual deterministic tractography approach, we investigated the micro- and macrostructural properties of the structural language network. Finally, we assessed the relationships between individually extracted tract values and verbal STM scores. We found significant correlations between volume measures of the right Uncinate Fasciculus and all three verbal STM scores. We also found significant associations between verbal STM scores and the left long segment of the Arcuate, the left Uncinate and the right Inferior Fronto-Occipital Fasciculi, although these did not survive FDR corrections. These findings suggest that the integrity of the right UF is associated with phonological and lexical-semantic verbal STM ability in aphasia and highlight the potential compensatory role of right-sided ventral white matter language tracts in supporting verbal STM after aphasia-inducing left hemisphere insult.

aphasia

stroke

verbal short-term memory

uncinate fasciculus

right hemisphere

DTI

The temporary maintenance of different types of information over the time course of their mental processing and representation is essential for multiple cognitive operations. This includes the input and output processing of linguistic information for effective communication. In aphasia, deficits in language processing at different levels of comprehension and production almost invariably coexist with impaired ability to retain linguistic representations in the short term (Martin, 2000). Therefore, a comprehensive understanding of verbal short-term memory (STM) deficits in aphasia at both the cognitive and neural levels could provide relevant insights into language-based theoretical models of verbal STM and inform aphasia research and clinical practice. To date, several behavioral studies have helped characterize general STM (see Murray et al., 2018, for a review) and specific verbal STM deficits in people with aphasia (PWA) at the phonological and semantic processing levels (see Martin, 2005, for a review). However, only limited research has been conducted to elucidate the brain correlates of verbal STM performance in aphasia. The present study seeks to fill this gap in the literature by characterizing the associations between important white matter tracts and verbal STM performance in aphasia.

STM can be thought of as the capacity to store a limited amount of information for a limited time, maintaining it in an active state (Cowan, 2008). However, STM is not a unitary maintenance store and can be viewed as part of working memory (WM), a related construct that emerged to account for different types of temporary memory and to incorporate processing in addition to storage operations (Cowan, 1996; 2008). The most dominant theoretical model in the field was proposed by Baddeley and Hitch (1974). This multi-component model entails a limited-capacity central executive control system and two storage systems, the phonological loop and the visuospatial sketchpad, that hold verbal and visual-spatial representations, respectively (see Baddeley, 2003 for a review). In this influential model, the temporary maintenance of language codes is mainly focused on the storage and processing of phonological information (Gupta & Tisdale, 2009). The phonological loop was put forth as a dual-component system with a phonological store that temporarily holds language memory traces, and a process of articulatory or subvocal rehearsal that keeps this information active and accessible. Support for the phonological loop is based on findings from immediate serial recall tasks showing (i) a phonological similarity effect reflected as shorter memory spans when items are phonologically similar (e.g., similar sounding letters and semantically unrelated but rhyming words) relative to sets with phonologically dissimilar items (Baddeley, 1966; Conrad & Hull, 1964), and (ii) a word-length effect where lists of multisyllabic words are harder to retain compared to single-syllabic word lists (Baddeley et al., 1975). While the phonological loop has been proposed as a “language learning device” that is crucial to facilitate foreign language acquisition through phonological encoding (Baddeley et al., 1998), Baddeley’s model is limited in accounting for the short-term maintenance and processing of semantic information (Baddeley, 1966; Cowan, 2008).

In the last decades, a growing amount of evidence has pointed towards a further division of verbal STM, with the retention of phonological and lexical-semantic information as two separable components (RC Martin et al., 1999; N Martin et al., 2020; Shivde & Anderson, 2011). Dissociations in verbal STM for phonological and lexical-semantic representations have been described across a variety of case studies presenting with selective STM deficits after brain damage. For instance, Martin, Shelton and Yaffee (1994) demonstrated diverging patterns of verbal STM performance in two patients with acquired brain damage who presented similarly reduced word spans. Specifically, the first patient showed reduced phonological yet normal semantic effects on word spans, whereas the second patient showed the reverse pattern. Moreover, the first patient also exhibited more impairment on a rhyme probe task assessing phonological STM relative to the second patient, who in turn evidenced better performance on a category probe task tapping lexical-semantic STM. In line with these findings, Majerus and colleagues (2004) described three patients who had recovered from Landau-Kleffner syndrome, a rare epileptic form of acquired aphasia, but still presented impaired phonological STM on nonword immediate serial recall and rhyme probe tasks, despite normal STM on a lexical-semantic category task. Of note, this dissociation has been corroborated across several studies (see Martin, 2005 for a review). All this evidence argues in favor of considering phonological and lexical-semantic STM as distinct capacities that deserve detailed examination, especially in clinical populations with acquired brain damage.

Importantly, the presentation of isolated verbal STM or language deficits alone is rare. Rather, impairments in both domains are generally found together (Koenings et al., 2011; Martin & Saffran, 1997; Papagno et al., 2007), in particular when lesions involve brain regions essential for sustaining the interaction and communication between language and memory systems (Roger et al., 2022). Indeed, while verbal STM deficits are uncommon in people with left hemisphere damage without aphasia or with right hemisphere damage (Jodzio & Taraszkiewicz, 1999; Kasselimis et al., 2013; Laures-Gore et al., 2011), they frequently coexist with language processing deficits in PWA secondary to brain injury (Martin, 2000). There is evidence that phonological and lexical-semantic STM are associated with different aspects of language processing and language learning in this population (Peñaloza et al., submitted). For instance, studies on sentence processing in aphasia have shown that phonological STM supports verbatim sentence repetition (Martin et al., 1994; Saffran & Marin 1975), whereas lexical-semantic STM has been associated with the elaboration of phrases during speech production (Martin & He, 2004; Martin & Schnur, 2019) and the initial retention of word meanings for their integration during verbal comprehension (Martin & He, 2004). Likewise, phonological and lexical-semantic STM have been associated with the ability to learn novel word forms and new word-referent mappings in PWA, respectively (Peñaloza et al., 2015; 2016). Moreover, it has been demonstrated that these two STM components make independent contributions to novel word learning in healthy individuals (Peñaloza et al., 2017) and that the functionality of phonological and lexical-semantic learning abilities in PWA can mirror the integrity of their phonological and lexical-semantic STM (Freedman & Martin, 2001). In addition, the integrity of verbal STM capacity has been associated with response to language treatment in PWA (Harnish et al., 2018) and interventions aiming to improve verbal STM capacity in this population have shown transfer effects to other linguistic abilities including verbal span and narrative discourse in some cases (Martin et al., 2020). Altogether, this evidence highlights the clinical relevance of the examination of verbal STM in PWA given its potential to inform the diagnosis and characterization of language impairment, and its prognostic value on language treatment outcomes. It also underscores the importance of conducting specific and sensitive assessments that measure verbal STM in terms of the type of linguistic information being encoded, whether lexical-semantic or phonological in nature (Martin et al., 2018), while considering how different language impairment and lesion profiles interact with specific lexical-semantic or phonological STM requirements (Martin & Ayala, 2004).

Although the behavioral research mentioned above has helped to characterize verbal STM abilities in aphasia, the number of studies investigating the neural underpinnings of verbal STM is much more limited. Both neuroimaging studies (Henson et al., 2000; Paulesu et al., 1993) and lesion studies (Basso et al., 1982; Baldo & Dronkers, 2006; Majerus et al., 2004; Pisoni et al., 2019; Takayama et al., 2004; Vallar et al., 1990; Warrington et al., 1971) have consistently pointed to the involvement of left-sided brain regions such as the posterior superior temporal gyrus (pSTG), the supramarginal gyrus (SMG) and the arcuate fasciculus (AF), as supporting phonological STM. On the other hand, the evidence concerning the neural basis of lexical-semantic verbal STM is even more limited. Various fMRI studies involving healthy subjects suggest that the involvement of the left inferior frontal gyrus (IFG) is important for this ability, as measured by tasks such as synonym judgements (Martin et al., 2003; Shivde and Thompson-Schill, 2004) or semantic anomaly judgements (Hamilton et al., 2009). Likewise, left IFG lesions appear to be predominantly present in patients presenting with lexical-semantic STM impairments (Hanten & Martin, 2000; Martin et al., 1994; Martin & He, 2004). In a recent study, Martin and colleagues (2021) addressed this question by applying multivariate lesion symptom mapping (LSM) in 94 acute left-hemisphere stroke patients. Results for phonological WM measured with the digit matching span task revealed the involvement of the SMG, as well as other cortical and subcortical regions including the left inferior frontal junction and the postcentral gyrus, possibly related to subvocal rehearsal mechanisms. In turn, regions related with lexical-semantic WM as measured by a category probe task, included the left IFG, the angular gyrus (AG) and the posterior superior temporal sulcus (pSTS). Although most regions associated with phonological and lexical-semantic WM in the study by Martin et al. (2021) are consistent with previous literature, the proximity –or even partial overlap– of brain regions related to these different verbal STM capacities represent a complicating factor in disentangling their neural underpinnings.

Modern neuroanatomical models of language processing (Friederici, 2015; Hickok & Poeppel, 2007; Jacquemot & Scott, 2006) propose that the dorsal and ventral language processing streams connect brain regions that are essential for phonological and semantic processing, respectively. Specifically, the arcuate fasciculus (AF) has been described as the main pathway supporting the dorsal stream, whereas the inferior fronto-occipital (IFOF), the inferior longitudinal (ILF) and the uncinate (UF) fasciculi are the main tracts related to the ventral stream for language processing (Catani et al., 2005; Dick et al., 2014).

Despite the existing evidence supporting the contributions of the abovementioned white matter pathways to phonological and semantic processing, the role of structural connectivity along those tracts in phonological and lexical-semantic STM has not yet been elucidated in aphasia. To the best of our knowledge, only the cortical lesion correlates of phonological and lexical-semantic verbal STM have been previously examined (RC Martin et al., 2021) despite the high vulnerability of white matter tracts to damage and disconnection following stroke. Thus, it is important to also assess the white matter structural markers of different verbal STM capacities in aphasia. The present study aimed to identify the white matter correlates of phonological and lexical-semantic STM in PWA following a left hemisphere stroke. We performed manual deterministic tractography to reconstruct the language-related white matter tracts bilaterally for each participant and estimated their macro- and microstructural properties by extracting the tract volume and fractional anisotropy (FA) values. We further examined the association between these DTI-derived measures and individual scores on phonological and lexical-semantic STM tasks to identify the neural underpinnings of verbal STM in this population, and to gain a better understanding about the white matter tracts that support these abilities after aphasia-inducing brain insults.

2.1 Participants

Participants were 19 chronic stroke patients (5 females, mean age = 60.5 ± 11.13) who were recruited at three local hospitals: Hospital Universitari de Bellvitge (n = 16), Hospital de l’Esperança (n = 2), and Hospital Comarcal de l’Alt Penedès (n = 1) (Barcelona province, Spain). All participants were diagnosed with aphasia at hospital admission and continued to present persistent aphasia at the time of study enrolment. One participant (P04) who showed scores within the normal limits across different language assessments (described in section 2.2) also presented complaints about their everyday language functioning relative to their pre-stroke abilities, indicating that language abilities were not fully recovered. The following inclusion criteria were employed: (i) age between 25 and 80 years, (ii) Spanish speaker, (iii) right-handed, (iv) unilateral cortical or cortico-subcortical stroke in the left hemisphere confirmed by medical records, (v) at least 6 months post stroke onset, (vi) preserved ability to follow instructions, (vii) eligible for MRI scanning. In addition, none of the participants presented with severe visual or auditory deficits, or a history of psychiatric or neurological disorders other than stroke. Table 1 presents the demographic and clinical information of the stroke participants. All participants provided their written informed consent to undergo study procedures approved by the Institutional Review Board of Hospital Universitari de Bellvitge (reference number: PR224/12) in accordance with the Declaration of Helsinki.

2.2. Language assessment

The diagnosis of aphasia, the evaluation of aphasia severity, as well as the clinical profile of language and speech abilities of the participants were based on the Spanish adaptation (Goodglass et al., 2005) of the Boston Diagnostic Aphasia Examination (BDAE-III) (Goodglass et al., 2001). The assessment of language abilities included the following BDAE-III subtests: (i) naming was assessed with the Boston Naming Test (BNT); (ii) repetition was evaluated with the Sentence repetition subtest; (iii) verbal comprehension was determined with the Word comprehension, Commands and the Complex ideational material subtests; and (iv) reading ability was evaluated using the Basic oral word reading and the Oral reading of sentences with comprehension subtests. Aphasia severity was determined using the BDAE Severity scale and the BDAE Language Competency Index which summarizes each participant’s scores on the main production and comprehension subtests. Finally, verbal comprehension was further assessed with the Token Test (De Renzi & Faglioni, 1978) and verbal fluency was evaluated with semantic fluency (animals) and letter fluency tasks (words beginning with the letter P) (Peña-Casanova et al., 2009). Table 2 presents the individual participants’ scores across all language assessments reported in this section.

2.3. Assessment of phonological processing and verbal STM

A selection of subtests from the Temple Assessment of Language and Short-Term Memory in Aphasia (TALSA; Martin et al., 2018) available in Spanish were administered to all participants to evaluate phonological processing and verbal STM, and composite scores were computed as done in previous aphasia studies (Peñaloza et al., 2016; 2017). Table 3 reports the scores of each participant for the described tests.

2.3.1. Phonological processing

Two TALSA subtests were administered to evaluate phonological processing. The rhyming judgments subtest required participants to decide whether pairs of words and nonwords presented auditorily rhymed or not. The phoneme discrimination subtest assessed the ability to discriminate if pairs of words and nonwords presented auditorily were the same or not. Each of these subtests was administered under two conditions with variations in memory load. The 1-second unfilled interval condition presented the words and nonwords of each pair separated by a 1 second delay, whereas the 5-second unfilled interval condition included a 5-second delay between the first and second stimulus of each word and nonword pair. Each condition in the rhyming judgments and the phoneme discrimination subtests included 20 words and 20 nonword pairs. Accuracy across conditions and tasks were summed up into a final phonological processing composite score for each participant.

2.3.2. Verbal STM

A set of TALSA subtests including verbal STM measures, either non-lexical (nonword repetition) or lexical (word repetition span, digit repetition span, word pointing span, digit pointing span), were administered to assess different aspects of verbal STM. The nonword repetition subtest assessed the ability to repeat 15 nonwords of 1, 2 or 3 syllables, created by altering one or two phonemes in real words. This subtest included two conditions that required the repetition of nonwords either after a 1-second or a 5-second interval as a way of manipulating STM load. A nonword repetition composite score was calculated by computing the percentage of correct responses in each interval condition and averaging these values across conditions. This composite score represents a measure of phonological STM with speech output as stimuli represented phonotactically legal “words” with no lexical-semantic representations. The word and digit repetition span tasks required participants to listen to a sequence of words or digits and repeat them immediately after its presentation, in the same order. The word and digit pointing span tasks required the participants to listen to sequences of words or digits and reproduce them in the same order by pointing at their corresponding pictures on a visual array of 9 possible items (the position of the items within the array was randomized across trials). Each repetition and pointing span task presented 10 strings of stimuli (words or digits) in each of 7 string lengths (1 item, 2 items, 3 items, etc.). In all cases, words and digit names were matched in syllable length, and sequences were generated from a finite set of 9 items, avoiding repetitions within the sequences. The final span size achieved in each task was calculated using the formula: string length at which at least 50% of the strings are recalled + (.50 x proportion of strings recalled in the next string length), as suggested in previous research (Shelton et al., 1992). The computed spans were then used to calculate two final composite spans: the repetition composite span which averaged the word and digit repetition spans and served as a measure of lexical-semantic STM with speech output; and the pointing composite span which averaged the word and digit pointing spans and tapped into lexical-semantic STM without speech output. It is worth noting that while the first measure requires the phonological route for repetition and speech output, the second measure can be considered a purer measure of lexical-semantic STM as it does not require speech output (Peñaloza et al., 2016). These three composite verbal STM scores representing phonological STM with speech output, lexical-semantic STM with and without speech output were the behavioral variables of interest for this study.

2.4. Neuroimaging data

2.4.1. MRI acquisition

All participants were scanned on a Siemens Magnetom 3T scanner with the Syngo MR B17 software and using a 32-channel head coil at Hospital Clinic, Barcelona (Spain). Diffusion-weighted images (DWI) were acquired with a spin-echo echo-planar imaging (EPI) sequence [TR = 5100 ms; TE = 80 ms; 48 axial slices; 64 directions, GRAPPA (generalized autocalibrating partially parallel acquisitions) acceleration factor 4; slice thickness = 2.5 mm; FOV = 23.5 cm; acquisition matrix = 94 x 94; voxel size = 2.5 mm³] with one non-diffusion (b = 0 s/mm2) and 64 diffusion weighted volumes (b = 1000 s/mm2). A high-resolution T1 (MPRAGE) image was also acquired in the same session (TR = 1970 ms; TE = 2.34 ms; slice thickness = 1.0 mm; acquisition matrix = 256 x 256; voxel size = 1.0 x 0.8 x 0.4 mm).

Table 1

–Demographic and clinical information of the participants.
Participants	Sex	Age (years)	Education (years)	TPO (months)	Aphasia type	Aphasia severity (1–5)	Aetiology	Lesion location (Left Hemisphere)	Lesion volume (cc)
P01	M	78	4	25	Global	1	Ischemic	Frontal regions, including IFG, MFG and SFG as well as the precentral and postcentral gyri and the rolandic operculum, temporal regions like STG and the ATL, the insula and the putamen.	72,93
P02	M	58	12	40	Transcortical motor	3	Ischemic	Parietal regions (post-central gyrus and superior and IPL), frontal regions, including IFG, MFG, SFG and the precentral gyrus, the rolandic operculum and the insula.	92,71
P03	M	61	18	26	Anomic	4	Ischemic	Frontal regions, including IFG, MFG and SFG as well as the precentral and postcentral gyri, the STG, the insula and the lentiform nucleus.	47,23
P04	M	62	11	24	Recovered	WNL	Ischemic	The temporal lobe, primarily the MTG and, to a lesser extent, the ITG and STG.	9,20
P05	M	51	5	20	Fluent	3	Ischemic	Mainly the left temporal lobe (STG and MTG) but also parts of the parietal (IPL and precuneus) and occipital lobes.	7,59
P06	F	75	6	20	Fluent	5	Ischemic	The insula, the lentiform nucleus and a portion of the IFG.	5,42
P07	M	63	8	41	Fluent	4	Ischemic	The frontal lobe -primarily the IFG and, to a lesser extent, the MFG- and the Insula.	17,26
P08	F	40	12	14	Broca’s	2	Ischemic	The Parietal lobe (IPL and Postcentral gyrus), Temporal regions (STG and MTG), the insula and the lentiform nucleus.	33,70
P09	M	47	8	6	Broca’s	3	Ischemic	The temporal lobe (STG, MTG, the TTG and the ATL), frontal regions (IFG and the precentral Gyrus) the postcentral gyrus and the insula.	31,29
P10	M	42	14	11	Mixed Nonfluent	1	Ischemic	The entire frontal and parietal lobes, some temporal regions like the STG, the TTG and the ATL, the insula, the lentiform nucleus and the para- and hippocampal regions.	186,40
P11	M	51	13	33	Fluent	5	Ischemic	Mainly the temporal lobe (STG, MTG, ITL and ATL) but also the IFG and the insula.	15,50
P12	M	69	8	24	Fluent	5	Ischemic	Mostly the parietal Lobe (SPL, IPL, postcentral gyrus) and, to a lesser extent, frontal (IFG, MFG, the precentral Gyrus) and temporal (STG, MTG, TTG) regions and the insula.	63,68
P13	M	61	11	34	Fluent	5	Ischemic	The temporal lobe (STG and MTG), the IPL and the insula.	5,63
P14	F	72	6	38	Fluent	5	Ischemic	A part of the lentiform nucleus and the insula.	0,48
P15	F	57	8	9	Nonfluent	3	Ischemic	The frontal lobe (IFG, MFG and precentral gyrus), the postcentral gyrus and the rolandic operculum, the STG and the insula.	9,67
P16	F	71	6	18	Mixed - Nonfluent	2	Undetermined	The frontal lobe (Precentral Gyrus and parts of IFG, MTG and SFG) and also the rolandic operculum, the postcentral gyrus, the IPL, the STG, the lentiform nucleus and the insula.	44,74
P17	M	58	5	36	Broca’s	2	Undetermined	The frontal lobe (precentral gyrus, IFG, MTG), the rolandic operculum, the STG and the insula.	23,18
P18	M	52	11	41	Transcortical motor	3	Ischemic	The frontal lobe (precentral gyrus and parts of the IFG, MTG ad SFG), the rolandic operculum, postcentral gyrus, the STG and the insula.	32,58
P19	M	73	14	31	Fluent	4	Ischemic	Most of the frontal lobe (IFG, MFG, precentral gyrus), parietal regions (IPL and postcentral gyrus), the rolandic operculum, some temporal parts (STG, MTG, ATL), the putamen, the middle occipital lobule, and the insula.	77,89
Demographic and clinical information for each participant. All the lesions described were strictly left-sided. Abbreviations: TPO = Time Post-stroke; CC = Cubic centimeters; M = Male; F = Female; WNL = Within Normal Limits; IFG = Inferior Frontal Gyrus; MFG = Inferior Middle Gyrus; SFG = Inferior Superior Gyrus; ITG = Inferior Temporal Gyrus; MTG = Inferior Temporal Gyrus; STG = Superior Temporal Gyrus; TTG = Transverse Temporal Gyrus; ATL = Anterior Temporal Lobe; IPL = Inferior Parietal Lobule.

Table 2

– General language evaluation scores of the participants.
Participants	BDAE-III									Token Test (36)	Animal Fluency	Letter Fluency
Participants	Language Index (100)	Word Reading (30)	Sentence Reading (10)	Comprehension in Reading (5)	BNT (60)	Sentence Repetition (10)	Word Comprehension (37)	Commands (15)	Complex Ideational material (12)	Token Test (36)	Animal Fluency	Letter Fluency
P01	24,16	6	NA	NA	22	3	26	11	8	14	0	1
P02	71,67	30	8	4	51	9	37	15	10	31,5	12	3
P03	90	30	10	5	41	10	37	15	12	35,5	13	4
P04	97,5	30	10	5	57	9	37	15	12	34,5	25	9
P05	46,65	30	9	3	42	9	29	11	4	28	13	7
P06	91,65	30	10	5	50	10	37	15	10	34	10	4
P07	79,15	30	10	5	53	9	36,5	15	11	31,5	12	6
P08	55,8	23	2	5	38	4	37	14	10	20	6	5
P09	71,65	30	8	5	48	7	37	15	10	34,5	13	2
P10	40,83	20	1	3	42	2	32	11	10	12,5	4	1
P11	95,85	30	10	5	58	8	37	15	11	35	16	9
P12	87,5	29	8	4	49	9	37	15	10	28	22	9
P13	83,33	30	10	5	51	10	36	14	12	32,5	14	10
P14	89,16	30	10	5	55	9	35	15	12	32	24	12
P15	60,835	27	10	3	46	8	34	15	5	29,5	6	3
P16	47,5	NA	NA	NA	39	9	34,5	13	7	19,5	5	3
P17	49,16	27	7	5	41	4	32	15	4	25,5	7	4
P18	64,16	30	10	5	54	9	37	14	6	28	13	5
P19	49,16	30	6	2	22	8	34	10	4	21	4	4
Bold numbers indicate scores that fall below normal limits according to normative data NEURONORMA and BDAE-III. Abbreviations: BDAE-III = Boston Diagnostic Aphasia Examination-Third Edition; BNT = Boston Naming Test; NA = Not Available.

Table 3 – Phonological processing and vSTM composite score for each patient.

Participant	Phonological Composite Score	vSTM
Participant	Phonological Composite Score	NW Repetition	Repetition Span	Pointing Span
P01	0,725	0,033	2,7	2,7
P02	0,6875	0,166	1,5	1,9
P03	1	0,6665	4,5	5
P04	1	0,865	5,7	6,2
P05	0,875	0,433	2,9	2,8
P06	1	0,53	3,8	3,6
P07	0,975	0,633	4,8	4,8
P08	0,975	0,3665	2,8	3,3
P09	0,975	0,466	4,2	4,7
P10	0,975	0,2995	2,2	1
P11	1	0,765	4,7	5,1
P12	0,9875	0,233	4,8	4,7
P13	1	0,735	5,4	5,6
P14	0,975	0,8	4,7	5
P15	0,8	0,3995	3,2	3
P16	0,75	0,433	3,2	4
P17	0,925	0,6665	3,1	3,6
P18	1	0,7995	4,4	4,5
P19	0,9375	0,565	3,8	3,3

Abbreviations: vSTM = verbal Sort-Term Memory; NW = Non-word

2.4.2. MRI preprocessing

Prior to preprocessing, all images were visually inspected to ensure the absence of any major artifact that could not be corrected in subsequent steps. Lesions were manually traced slice-by-slice for each participant on their T1 structural brain images by GO using the MRIcron software (http://www.mccauslandcenter.sc.edu/mricro/mricron) and were further verified by an experienced neurologist (see Figure 1 for the lesion overlay map across participants). Next, as the first step in the preprocessing, T1-weighted images were warped and adjusted to the Montreal Neurological Institute (MNI) space using the Statistical Parameter Mapping software (SPM12, Wellcome Trust Centre for Neuroimaging, London, UK, www.fil.ion.ucl.ac.uk/spm/). The warps obtained were then used to normalize the lesion masks to MNI space. MRIcron was again employed to extract individual total lesion volumes and the xjview toolbox (https://www.alivelearn.net/xjview) was used to identify anatomical structures affected by stroke in each participant (Table 1).

---------------------- Please, place Figure 1 around here -------------------------

All diffusion images were pre-processed using the FMRIB Software Library (FSL www.fmrib.ox.ac.uk/fsl/fdt) and the Diffusion Toolkit software (DTK) (Wang et al., 2015). DWI data were processed as in previous studies from our team (Olivé et al., 2022; Vaquero et al., 2021) following these steps: (i) eddy-current correction using the FMRIB Diffusion Toolbox (FDT), part of FMRIB Software Library (FSL www.fmrib.ox.ac.uk/fsl/fdt); (ii) brain extraction using FSL Brain Extractor Tool (Smith, 2002; Smith et al., 2004; Woolrich et al., 2009) with 0.3 as threshold value; (iii) rotation of the b-vectors; (iv) reconstruction of the diffusion tensors using DTK (Wang et al., 2015); and (v) whole-brain deterministic tractography using DTK with 35 degrees as maximum curvature and a minimum FA threshold of 0.2.

2.4.3. Tract dissections

Manual deterministic tractography was performed on preprocessed images focusing on four main language-related white matter tracts: the three segments of the arcuate (AF), inferior fronto-occipital (IFOF), inferior longitudinal (ILF), and uncinate (UF) fasciculi. These tracts were dissected bilaterally for each patient in native space using the Trackvis software (v.0.6.0.1, http://trackvis.org/) by manually placing Regions of Interest (ROI) as described in previous research (Catani & Thiebaut de Schotten, 2008; see Olivé et al., 2022 for ROI placement examples of the tracts dissected here).

AF. The three segments of the AF were dissected using a three-ROI approach, each drawn in a single slice as described in previous studies (Catani et al., 2005; Lopez-Barroso et al., 2013). The first ROI was delineated in the coronal plane encompassing the fibers going to IFG, including BA44 and 45 (frontal); the second ROI was drawn in the axial plane covering the white matter fibers traveling to the superior temporal gyrus (temporal); and the third ROI was depicted on the sagittal view, covering the supramarginal and angular gyri (parietal). These ROIs were combined to reconstruct the three subdivisions of the AF: the long (fronto-temporal), the anterior (fronto-parietal), and the posterior (temporo-parietal) segments.

ILF, UF and IFOF. To delineate these three white matter pathways supporting the ventral stream for language processing (Hickok & Poeppel, 2007; Rauschecker & Scott, 2009), we combined three ROIs according to previous studies (Catani & Thiebaut de Schotten, 2008). The first ROI was placed axially at the level of the anterior temporal lobe (temporal ROI) encompassing an average of 5 slices; the second one on the anterior floor of the external/extreme capsule covering an average of 3 slices (frontal ROI); and the third one on the region located between the occipital and temporal lobes covering an average of 7 slices (occipital ROI). To define the tracts of interest, we applied a two-ROI approach: the ILF was comprised by fibers going through the temporal and occipital ROIs; fibers going through both temporal and frontal ROIs were part of the UF; and the fibers crossing the frontal and occipital ROIs formed the IFOF.

Additionally, exclusion ROIs were used for each of the tracts in order to remove any artefactual fibers when present, as commonly done in manual reconstructions (Elmer et al., 2019; Vaquero et al., 2021). For visualization purposes, the streamlines were rendered using the “tube” render option of TrackVis with a radius of 0.15 mm and 10 sides. A depiction of dissections for all participants is provided in Figure 2.

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Output measures extracted from every tract and hemisphere included macrostructural (volume) and microstructural (Fractional Anisotropy, FA) values. Tract volumes are thought to reflect the number of times a streamline could be reconstructed between two brain regions. (Jones et al., 2013). Although this measure does not indicate the real fiber count of the tract (Jones et al., 2013), it has been used as a proxy of the tracts’ macrostructure in several DTI studies (Catani et al., 2007; Olivé et al., 2022; Wan et al., 2012) and it is thought to be modulated by properties of the tract including fiber-packing or myelination (Vaquero et al., 2021). As for microstructure, our DTI marker of interest was fractional anisotropy (FA). It reflects the degree of anisotropy (Winston, 2012) and numerous intrinsic characteristics including fiber count and dispersion, packing density, myelination or membrane permeability. FA has also been widely used in the DTI literature (Lebel & Beaulieu, 2009; Molinuevo et al., 2014) and, together with tract volume, it is considered to be a sensitive measure to explore individual differences (Vaquero et al., 2017). Furthermore, these measures are not only useful for studying healthy anatomy; they also provide valuable information about brain structural connectivity characteristics after an insult such as a stroke or a tumor (François et al., 2019; Simó et al., 2015), and have been previously used for investigations in PWA (Forkel & Catani, 2018; Ivanova et al., 2016; Schlaug et al., 2009; Yang et al., 2017).

2.5. Statistical analyses

Statistical analyses were conducted using the IBM SPSS software (v25.0). To assess the relationships between white matter macro- and microstructural organization and verbal STM performance in PWA, Pearson correlations were calculated to examine associations between measures of phonological and lexical-semantic STM (nonword repetition, pointing span, and repetition span composite scores) and both mean volume and FA values extracted for each tract and hemisphere. Of note, specific tracts could not be reconstructed for some participants (see supplementary Table 1 for details on missing tracts per hemisphere). In such cases, volume was computed as zero, whereas FA was removed from the correlation analyses.

The False Discovery Rate (FDR) correction was used to adjust for multiple comparisons and all p values are reported after this correction. FDR corrections were performed separately for each tract and white matter related measure (6 correlations per tract and measure: 2 hemispheres x 3 verbal STM scores). Additionally, an FDR correction was performed for volume and FA separately with all tracts (32 correlations per measure: 6 tracts/segments x 2 hemispheres x 3 verbal STM scores).

Overall lesion volume was significantly correlated with nonword repetition composite scores (r = -0.498, p = .03), repetition span (r = -0.626, p = .004) and pointing spans (r = -0.480, p = .038). Likewise, aphasia severity (as measured by the BDAE Language Competence Index) was significantly correlated with all three measures: nonword repetition scores (r = 0.615, p = .005), repetition spans (r = 0.827, p < .001) and pointing spans (r = 0.883, p < .001). Thus, we further examined the contributions of overall lesion volume and aphasia severity to any relationships between white matter measures and verbal STM scores, FDR-corrected significant correlations were reanalyzed as partial correlations using normalized total lesion volume and the BDAE Language Competence Index as control covariates. Of note, the BDAE Language Competence Index was preferred over the traditional BDAE aphasia severity scale for this analysis because captures a larger individual variability in terms of overall language impairment (range 0 – 100 percentile scores) while accounting similarly for both comprehension and expression abilities. The BDAE aphasia severity scale allows one to classify severity only on a limited 5-point scale which is largely determined by fluency in language production relative to verbal comprehension (Goodglass et al., 2005).

3.1. White matter tract volume and verbal STM

The right UF emerged as the main white matter tract involved in verbal STM in our cohort of PWA, with volume showing significant correlations with all three measures of verbal STM (FDR corrected). Specifically, the right UF volume was significantly correlated with non-word repetition (r = 0.680, p = .006), pointing span (r = 0.523, p = .044), and repetition span (r = 0.560, p = .039) scores after the FDR correction was performed independently for every tract (number of comparisons: 6). Figure 3 provides a depiction of these significant associations. Importantly, only the correlation between the right UF volume and non-word repetition scores (r = 0.680, p = .036) survived FDR corrections for the multiple comparisons performed for all tracts and hemispheres (number of comparisons: 36). Similarly, partial correlations controlling for both lesion volume and BDAE Language Competence Index corroborated this significant association between the right UF volume and non-word repetition scores (r = 0.595, p = .012) although its correlations with pointing span (r = 0.426, p = .088), and repetition span (r = 0.451, p = .069) scores became statistically non-significant.

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It is worth noting a few additional significant associations between white matter measures and verbal STM scores, albeit these were FDR uncorrected. Specifically, the volume of the long segment of the left AF was significantly correlated with all verbal STM measures: non-word repetition (r = 0.468, p = .043), pointing span (r = 0.506, p = .027) and repetition span (r = 0.541, p = .017) scores. Additionally, the volumes of the left UF (r = 0.484, p = .036) and the right IFOF (r = 0.487, p = .035) showed significant correlations with nonword repetition scores. No other significant correlations were found between white matter tract volumes and verbal STM scores (all p values ≥ 0.05 FDR uncorrected). Uncorrected significant correlations at the .05 level are depicted in Supplementary Figure 1.

3.2. White matter tract FA values and verbal STM

FA values were not significantly correlated with any of the verbal STM measures for any of the tracts / hemispheres in the present sample (p ≥ 0.05 in all cases). The results from all correlations performed for volume and FA measures are showed in Supplementary Tables 2 and 3, respectively.

The aim of this study was to investigate the white matter structural correlates of phonological and lexical-semantic STM in post-stroke aphasia. Manual deterministic tractography was used to reconstruct the main language-related white matter pathways in the brain including the AF, UF, IFOF, and the ILF. White matter tract volume and FA values were extracted bilaterally for each tract and their relationships with phonological and lexical-semantic STM scores were evaluated before and after partialling out the effects of aphasia severity and overall lesion volume. We found that white matter tract volumes, but not FA values, were associated with verbal STM in PWA, suggesting that macro-structural properties of white matter fibers are more sensitive to capture individual differences in verbal STM performance in chronic aphasia. In particular, we found a strong association between the right UF volume and all measures of phonological and lexical-semantic STM. Among these, the strongest association was found between the right UF volume and nonword repetition scores after controlling for both overall lesion volume and aphasia severity, suggesting a role of the right UF in phonological verbal STM in chronic aphasia, irrespective of the extent of overall brain damage and aphasia severity. Similarly, the volume of other language-related tracts showed significant correlations with verbal STM scores, but only when uncorrected for multiple comparisons. This was the case of the long segment of the left AF, the volume of which correlated significantly with all three verbal STM measures, and the left UF and the right IFOF volumes which were both associated with nonword repetition scores.

It is worth considering these findings in the light of current neurocognitive models of language processing and verbal STM. Based on the functional specialization of the dorsal and ventral pathways proposed by these models (Friederici, 2015; Hickok & Poeppel, 2007; Jacquemot & Scott, 2006), one would expect an association between dorsal white matter tracts and nonword repetition composite scores reflecting phonological STM on one hand, and between ventral pathways and repetition and pointing composite spans reflecting lexical-semantic STM on the other. Further, when considering hemispheric lateralization, one would also expect that phonological STM would rely on left lateralized white matter tracts as the dorsal stream for phonological processing is assumed to be strongly left–hemisphere dominant, and that lexical-semantic STM would be supported by ventral tracts in both hemispheres as the ventral stream for semantic processing should be bilaterally organized in neurotypical individuals (Bajada et al., 2015; Hickok & Poeppel, 2007). Given these considerations of functional and hemispheric / neuroanatomical specialization, the expectations mentioned above would be particularly relevant to patients examined in the acute/subacute phase after stroke as the functionality of verbal STM (as any other cognitive ability) at this phase would be predominantly reflective of neural integrity (Martin et al., 2021). Nonetheless, our sample exclusively included participants with chronic aphasia, who may have developed specific STM strategies to compensate for their language and verbal STM dysfunction resulting from stroke. Thus, the associations between verbal STM components and the specific white matter tracts and their hemispheric lateralization in this patient sample may reflect some degree of post-stroke functional reorganization. With this consideration in mind, our findings were partially aligned with the above described expectations in that (i) the volume of the right UF was significantly correlated with both measures of lexical-semantic STM (FDR corrected), and (ii) the volume of the long segment of the left AF was also significantly correlated (although FDR uncorrected) with a measure of phonological STM (nonword repetition composite score) and a measure of lexical-semantic STM (repetition composite span) that also partially taps into repetition processes, relying on phonology and the mapping of sounds to articulation. These findings support the classical functional division of the dorsal and ventral streams and suggest that the integrity of white matter pathways within both routes can inform about the short-term maintenance of phonological and lexical-semantic representations after left hemisphere damage. Moreover, they also suggest that the right UF may still support verbal STM for lexical-semantic representations even after damage to the left UF tract and/or its cortical terminations. This interpretation aligns with the possibility of right hemisphere compensation which may capitalize on the bilateral organization of the ventral stream for semantic processing (Bajada et al., 2015; Hickok & Poeppel, 2007).

However, not all correlations between dorsal and ventral white matter tracts and verbal STM measures were in line with the potential associations expected according to models of the dorsal and ventral pathways (Hickok & Poeppel, 2007; Dick & Tremblay, 2012). Indeed, ventral white matter pathways were also associated with phonological STM which would be presumably supported by the dorsal stream, and dorsal white matter pathways were associated with lexical-semantic STM which would be expected to be supported by the ventral stream. Specifically, we found significant correlations between phonological STM and both the volume of the UF (right UF FDR corrected, and left UF uncorrected) and the right IFOF (FDR uncorrected). In addition, we also found significant correlations between lexical-semantic STM (pointing composite span) and the volume of the long segment of the left AF (FDR uncorrected).

One possible interpretation of these results is that this dorsal-phonological versus ventral-semantic dichotomy may not be as clear as previously proposed, at least in terms of their contributions to different components of verbal STM. In fact, this would make sense at the anatomical level given the proximity –or even partial overlap– of the cortical regions that have been associated with phonological and lexical-semantic STM (Martin et al., 2021). Moreover, different white matter tracts, either from dorsal or ventral language pathways, have terminations in these regions and could constitute structural support for verbal STM abilities. More specifically, UF is a long-range white matter tract connecting temporal regions like the anterior temporal lobe (ATL), the uncus and entorhinal and perirhinal cortices with the orbitofrontal and lateral prefrontal cortices, the frontal pole and the anterior cingulate gyrus (Dick et al., 2014; Thiebaut de Schotten et al., 2012; Von der Heide et al., 2013). Although its role is still debated (Papagno et al., 2011), the UF is considered as part of the ventral stream of language processing (Hickok & Poeppel, 2007), thought to support the mapping of sound-based speech representations to distributed conceptual representations (Saur et al., 2008). Two of the functions most ascribed to this tract are naming and lexical-semantic processing, which have also been attributed to the ATL (Dick & Tremblay, 2012; Papagno et al., 2011). Although it has received less attention beyond its role in language, the UF has also been linked to memory functions since it connects the ATL, believed to contribute to semantic memory, and the entorhinal cortex that is related to episodic memory functions carried out in the hippocampus (Von der Heide et al., 2013). Moreover, microstructural properties of the UF have been repeatedly associated with auditory-verbal memory both in children (Mabbott et al., 2009; Schaeffer et al., 2014) and in adults (Diehl et al., 2008; McDonald et al., 2008), and with lexical-semantic learning that requires the mapping of novel words onto basic novel conceptual representations (Ripollés et al., 2017). On the other hand, the left AF has been consistently and repeatedly associated with language functions (Catani et al., 2005; Catani & Mesulam, 2008) and it is thought to constitute the core of the dorsal language-processing stream (Catani et al., 2005; 2007). In particular, the long segment of the AF connects the pSTG with the IFG (Dick et al., 2014) and has been related to auditory-motor integration, articulation, repetition, language imitation and translation, sentence processing, or verbal WM (Elmer & Kuhnis, 2016; Elmer et al., 2019; Lopez-Barroso et al., 2013; Meyer et al., 2014; Saur et al., 2008; Vaquero et al., 2017). Both tracts present terminations in inferior frontal regions, which have been associated with both phonological (Chein & Fiez, 2001; Yue et al., 2018) and lexical-semantic verbal STM (Lewis-Peacock et al., 2012; Martin et al., 2003; Shvide & Thompson-Schill, 2004). In the same vein, Sajid and colleagues (in preparation) investigated the effective connectivity between left-hemisphere regions involved in auditory speech repetition. Although a direct parallel between functional and structural connectivity cannot be easily drawn, their findings suggest that the AF may not be the only critical structure involved in repetition processes, as has been often thought, since the motor component of word repetition can be also initiated or carried out by other tracts (Forkel et al., 2020; Hanley et al., 2004). This idea is supported by previous descriptions stating that the connection between the posterior superior temporal sulcus (pSTS) and IFG –at both functional and structural levels– can be supported in alternative ways in addition to the direct physical link provided by AF (Catani et al., 2005; Friederici, 2015). This adds to the debate of the specificity and parcellation of the neural underpinnings of phonological and semantic processes.

The fact that alternative pathways could communicate particular brain regions involved in different aspects of verbal STM (such as the inferior frontal regions) albeit potentially in a less functionally specialized manner, also allows for consideration that the associations between STM and white matter tracts found in the current study might reflect adaptation processes following stroke. Indeed, brain plasticity mechanisms could account for the possibility that white matter tracts not intrinsically related to phonologic or lexical-semantic STM could assume these functions following acquired brain injury. For instance, Duffau and colleagues (2009) argued that the UF is not systematically essential for language, as other tracts of the semantic ventral stream (such as the IFOF) can compensate for it in case of functional alterations. This possibility is further supported by studies showing that even dorsal and ventral pathways can compensate each other and carry out functions typically ascribed to the other language stream under high demand or functional constraints (Lopez-Barroso et al., 2011; Yeatman et al., 2012) and after brain damage (Rauschecker et al., 2009; Torres-Prioris et al., 2019). In addition, the fact that right hemisphere tracts correlated with phonological STM measures relying on a predominantly left-lateralized dorsal stream, is in line with multiple studies showing right hemispheric recruitment reflecting compensatory changes in the right hemisphere in PWA following a left hemispheric stroke (see Kiran & Thompson, 2019 for a review).

Notably, one of the questions that remains open is whether the involvement of the right-hemisphere white matter tracts –especially the UF– in different aspects of verbal STM is intrinsic to these cognitive processes or whether it occurs as an adaptive strategy to compensate for the lesions observed in the left hemisphere. The premorbid status and volume of right hemisphere tracts might be an important factor defining whether the contralesional hemisphere engages in post-stroke recovery (Kiran & Thompson, 2019; Stefaniak et al., 2021). In line with this idea, Forkel and colleagues (2014) showed that the volume of the right AF was a predictor of the degree of severity of language impairment 6 months after a left hemispheric stroke. As regards to the functional laterality of the UF, the study from Emch and colleagues (2019) reported a bilateral frontal activation related to verbal WM, which might indicate the involvement of the right UF in healthy individuals. As for its structural lateralization, the previous literature shows inconclusive results regarding the hemispheric differences of the UF (Von der Heide et al., 2013). However, the fact that the UF is not a strongly left-lateralized structure (as opposed to other language-related tracts, such as the long segment of the AF) might somehow facilitate its right recruitment after a left hemisphere lesion. In support of a model of compensatory reorganization of verbal STM, our study participants were people with chronic post-stroke aphasia, with several of them showing large lesions in the left hemisphere. For these cases, engaging alternative ways of connections such as right-hemisphere homologous tracts, might be the only way of compensation of the sustained left-sided damage. To shed some light on this issue, in future studies one could employ the present methodology in a follow-up of PWA through the acute and subacute phases into the chronic stage.

Considering the cognitive level, another possible interpretation would be that PWA, due to the language processing limitations caused by their brain injuries, may adopt compensatory strategies to complete the verbal STM tasks. In other words, they could rely on relatively more spared phonological mechanisms to perform lexical-semantic verbal STM tasks or vice versa. In fact, it has been previously described that the phonological representation of a word can help reactivate its semantic representation if it is not preserved at the time of evaluation, whereas purely phonological elements might be better remembered if they bear semantic implications (Jones & Macken, 2015; RC Martin et al., 2021).

It is important to note that the potential interpretations presented above are not mutually exclusive. Rather, they are in line with the redundant nature that the brain exhibits both at structural and functional levels, which in some cases allows it to retain the functionality of cognitive processes after injury (Duffau, 2006).

One should also acknowledge some limitations in the current research, including the restricted sample size which may have reduced the statistical power to identify further relevant associations between white matter tracts and phonological and lexical-semantic STM. This may have influenced the number of significant correlations that finally survived the FDR corrections. Moreover, as most of the associations discussed above were uncorrected for multiple comparisons, interpretations should be taken with caution. However, regarding the variables studied, the Language Competence Index is not independent of the verbal STM scores. Likewise, higher lesion volume increases the likelihood that a given tract is damaged. Thus, the partial correlations used may have somewhat underestimated the associations between the structural and behavioral variables of interest. Another important limitation is the lack of a control group, which would have helped to clarify the involvement of the right UF in healthy populations. Furthermore, some aspects of the MRI data acquisition and pre-processing steps of the diffusion images could be further improved. For instance, future studies could apply a denoising step or the new FSL eddy tool to obtain even better-quality images improving the final result. Finally, the massive lesions suffered by some of the participants in this study prevented us from reconstructing some of the tracts in the left hemisphere in a notable proportion of the sample. Although this hindered the identification of potential contributions of left hemisphere tracts to verbal STM, our main interest was to identify the white matter tracts that support verbal STM in people with chronic post-stroke aphasia and this constraint is inherent to their condition. Future work should complement our findings by studying white matter tract properties in larger samples of individuals with or without aphasia, in both the acute and chronic states of stroke, and with different lesion extents, in comparison to a healthy control group. This would help establish if left hemisphere structures intrinsically support vSTM or to understand if there are tipping points of lesion extent and time post onset that determine the engagement of right tracts over left hemisphere ones. In summary, future research could further corroborate to what extent the associations reported here are reflective of processes of plasticity and reorganization.

Our findings revealed a strong association between the volume of the right UF and measures of phonological and lexical-semantic STM, with the strongest association being with nonword repetition scores. This suggests that the right UF supports verbal STM above and beyond lesion volume and aphasia severity in chronic aphasia. These results contribute to a better understanding of the white matter correlates of verbal STM after left hemisphere damage, and cerebral plasticity and compensatory mechanisms in chronic aphasia.

Acknowledgments: We would like to thank all participants and their families for their valuable time and commitment to take part in this study.

Funding

This study was supported by a grant of the Language Learning Small Grants Research Program awarded to Antoni Rodríguez-Fornells. Guillem Olivé acknowledges the Government of Andorra from a predoctoral grant, ATC0XX-AND-2021/2022. Claudia Peñaloza was supported by the Juan de la Cierva-Incorporación 2018 program (IJC2018-037818) funded by the Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación MCIN/AEI/ 10.13039/501100011033. Lucía Vaquero was supported by the Program to Attract Talented Researchers to Include them in Research Groups within the Autonomous Region of Madrid 2018–Modality 2 (2018-T2/BMD-10991) funded by Madrid Region’s Consejería de Educación, Universidades, Ciencia y Portavocía. Matti Laine was supported by the Academy of Finland (grant number 323251). Research reported in this publication was supported in part by the National Institute on Deafness and other Communication Disorders of the National Institutes of Health (grants numbers R01DC013196 and R01DC016094) awarded to Temple University (PI: Nadine Martin)

Disclosures

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of Competing Interest

None of the authors has competing interests to declare.

CRediT author statement

Guillem Olivé: Methodology, Investigation, Formal Analysis, Writing – Original Draft, Visualization Claudia Peñaloza: Conceptualization, Investigation, Data Curation, Formal Analysis, Writing – Original Draft, Writing – Review & Editing Lucía Vaquero: Formal Analysis, Writing – Original Draft, Writing – Review & Editing Matti Laine: Conceptualization, Writing – Review & Editing Nadine Martin: Conceptualization, Resources, Writing – Review & Editing Antoni Rodriguez-Fornells: Conceptualization, Resources, Writing – Review & Editing, Funding Acquisition.

Availability of data and material

Anonymized data will be shared by request from any qualified investigator.

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The right uncinate fasciculus supports verbal short-term memory in aphasia

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1. Introduction

2. Material And Methods

2.1 Participants

2.2. Language assessment

2.3. Assessment of phonological processing and verbal STM

2.3.1. Phonological processing

2.3.2. Verbal STM

2.4. Neuroimaging data

2.4.1. MRI acquisition

2.4.3. Tract dissections

2.5. Statistical analyses

3. Results

4. Discussion

5. Conclusions

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