# An insight from the default mode network in patients with amnesia following left thalamic infarction involving the mediodorsal nucleus and mammillothalamic tract

Lola Danet, Emmanuel J Barbeau, Marie Lafuma, Fabrice Bonneville, Igor Sibon, Jean-François Albucher, Jérémie Pariente, Patrice Peran

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## To cite this version:

Lola Danet, Emmanuel J Barbeau, Marie Lafuma, Fabrice Bonneville, Igor Sibon, et al. An insight from the default mode network in patients with amnesia following left thalamic infarction involving the mediodorsal nucleus and mammillothalamic tract. Cortex, 2025, 183, pp.220-231. 10.1016/j.cortex.2024.10.024. hal-04992947

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Submitted on 16 Mar 2025

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Special Issue ‘new Perspectives in Neuropsychology: from Biology to Society’:
Research Report

An insight from the default mode network in
patients with amnesia following left thalamic
infarction involving the mediodorsal nucleus and
mammillothalamic tract

<figure><img src="image_4.png" /></figure>

Lola Danet <sup>a,b,c,*</sup>, Emmanuel J. Barbeau <sup>b</sup>, Marie Lafuma <sup>a,c</sup>, Fabrice Bonneville <sup>a,c</sup>, Igor Sibon <sup>d</sup>, Jean-François Albucher <sup>a,c</sup>, Jérémie Pariente <sup>a,c,1</sup> and Patrice Peran <sup>a,1</sup>

ARTICLE INFO

Article history:

Received 5 May 2024

Reviewed 19 June 2024

Revised 26 July 2024

Accepted 22 October 2024

Published online 17 December 2024

Keywords:

Thalamus

Mediodorsal nucleus

Mammillothalamic tract

Memory

Functional connectivity

A B S T R A C T

The role of the medial part of the thalamus, and in particular the mediodorsal nucleus (MD) and the mammillothalamic tract (MTT), in memory has long been studied, but their contribution remains unclear. While the main functional hypothesis regarding the MTT focuses on memory, some authors postulate that the MD plays a supervisory executive role (indirectly affecting memory retrieval) due to its dense structural connectivity with the prefrontal cortex (PFC). Recently, it has been proposed that the MD, MTT and PFC form part of the DMN the default mode network (DMN). Due to the theoretical presence of MD and MTT in the DMN, we aimed to show the effect of thalamic lesions on functional connectivity (FC) and its putative role in cognitive impairment.

We recruited 12 patients with left thalamic infarction and 12 matched healthy controls. They underwent neuropsychological assessment including memory tasks, morphological 3D MRI and resting state fMRI. A ROI-to-ROI method was used for group-level FC analyses.

Patients had lesions in the MD and ventrolateral nuclei, with a damaged mammillo-
thalamic tract (MTT) in seven of them. They showed lower performance than controls on
verbal memory, executive function and language tests, with more impairment in memory,
working memory, semantic verbal fluency and attention in the MTT-damaged patients.
Contrast analyses between patients and matched controls showed lower FC in the ventral
and dorsal DMN. Correlation analyses (patients and controls pooled) showed i/a positive
correlation between memory and DMN, and ii/that MTT volume correlated with decreased

<footnote><sup>a</sup> Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France</footnote>

<footnote><sup>b</sup> Brain & Cognition Research Center (CerCo), CNRS-University of Toulouse Paul Sabatier, Toulouse, France</footnote>

<footnote><sup>c</sup> Neurology Department, Toulouse University Hospital, Toulouse, France</footnote>

<footnote><sup>d</sup> Stroke Unit, Department of Neurology, CHU Bordeaux, Bordeaux University, Bordeaux, France</footnote>

<footnote>This article is part of a special issue entitled: Memory and the social brain published in Cortex.</footnote>

<footnote>* Corresponding author. Inserm ToNIC U1214, Pavillon Baudot, CHU Purpan, Place du Dr Baylac, 31059, Toulouse, France.
E-mail address: lola.danet@inserm.fr (L. Danet).</footnote>

<footnote n="1">¹ Both authors contributed equally.</footnote>

<footnote>https://doi.org/10.1016/j.cortex.2024.10.024</footnote>

<footnote>0010-9452/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
functional connectivity in the dorsal DMN, whereas there was no correlation with MD lesion volume.

These results suggest that both the memory impairment and the DMN functional change we observed may reflect an effect of the MTT lesion rather than MD damage.
© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

# 1. Introduction

The role of the mediodorsal thalamus (MD) in memory has been extensively studied (Aggleton et al., 2011; Cipolotti et al., 2008; Danet et al., 2015, 2017; Pergola et al., 2012; Tu et al., 2014; Zoppelt et al., 2003). Using the Multi-Effect Multi-Nuclei (MEMN) model to demonstrate how the thalamic nuclei are involved in memory, Aggleton et al. postulate that a disconnection-diaschisis-like hypothesis (decreased cortical connectivity/activity after subcortical injury) could be a mechanism explaining memory impairment after thalamic lesion (Aggleton et al., 2011).

Assuming functional thalamo-cortical change as a cause of memory loss, Aggleton and colleagues identified prefrontal cortex (PFC) and cingulate cortices as the most vulnerable cortical sites (Aggleton et al., 2011). As such, cognitive impairments associated with a MD lesion could be due to anatomical or functional disconnection from larger networks (Georgescu et al., 2020; Schmahmann & Pandya, 2008). In the last years, the MD, and possibly the mammillothalamic tract (MTT) connecting the mamillary body to the thalamus, have been identified as key structures in the default-mode network (DMN) (Alves et al., 2019). The DMN is a network of functionally correlated brain areas identified by the contrast between task-related and resting-state fMRIs. The DMN is thought to be involved in self-referential functions such as mentalizing, remembering or decision making, in a stimulus independent way (Buckner et al., 2008; Wen et al., 2020). More precisely concerning memory, some authors postulated that DMN is involved in retrieval of episodic memories from percept to semantics (Kaefer et al., 2022). Initially, the DMN regions include the ventral medial PFC, posterior cingulate and retrosplenial cortex, inferior parietal lobule, lateral temporal cortex, dorsal medial PFC and hippocampal formation (Buckner et al., 2008). Using an innovative methodology, Alves et al. included subcortical structures to describe DMN connectivity through different types of analyses (rs-fMRI, tractography, graph theory). The authors proposed that the set of nodes corresponding to the DMN initially described in the literature is incomplete, omitting subcortical structures due to a misalignment of functional regions between subjects when only structural coregistration is performed. Finally, they move from a corticocentric view of the DMN to a more comprehensive DMN model including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and MD nuclei of the thalamus and a set of reconstructed tracts including the MTT.

In previous studies, we showed that patients with a left thalamic stroke reaching the MD had a memory impairment

(Danet et al., 2015, 2017). These results were corroborated by other studies (Pergola et al., 2012; Tu et al., 2014). Interestingly, we did not observe any correlation between memory performance and volume loss in the MD, but patients with lesions reaching the MTT had lower memory performance and the MTT volume loss was correlated with memory results. In the present study, we hypothesized that i/memory impairment presented by patients with a left thalamus infarct reaching the MD and, for some of them, the MTT, is related to a decreased FC. Moreover, we postulated that ii/functional change in the DMN would be a possible indirect consequence of the MD and MTT damage. To do so, we used rsfMRI to measure FC due to its reliability and robustness to assess the functional integrity of the DMN and other functional networks (Buckner et al., 2008).

# 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.

## 2.1. Ethics

All participants provided written informed signed consent to take part in this study, which was approved by the local institutional review board (Comité de Protection des Personnes Sud-Ouest et Outre-Mer no. 2-11-04). No part of the study procedures or analyses was pre-registered prior to the research being conducted.

## 2.2. Participants

We prospectively recruited between 2011 and 2013 patients at least 3 months after a single unilateral left thalamic infarct in the stroke units of Toulouse and Bordeaux (France). The inclusion and exclusion criteria were established prior to data analysis. Patients recruited had no previously known neurovascular, inflammatory or neurodegenerative diseases. Brain infarcts were diagnosed on a diffusion-weighted MRI scan during the acute phase. In the same period, we also recruited healthy controls matched one by one for age and education (number of school years) to the patient group. Participants presenting another neurological disease or taking psychotropic drugs daily could not be included in the study. There was no age limit. Due to its rarity, the sample size was approximately 10 patients and was clinically estimated according to the prevalence of first isolated unilateral thalamic stroke.
Following inclusion, all participants underwent a clinical examination, a neuropsychological assessment as well as structural and rs-fMRI scans. These investigations were performed in a single day, in the same order, and in the same place (ToNIC Inserm laboratory, Toulouse). Collected MRI raw data are not publicly available due to legal and ethical barrier (for the re-use of the data a new ethical agreement is necessary).

## 2.3. Neuroimaging acquisition

MRI images for the study were acquired on a 3T scanner (Philips Achieva) for structural and resting state fMRI.

Thalamic lesions were documented using a three-dimensional T2-weighted sequence (1\*1\*1-mm voxel size, echo time = 337 msec, repetition time = 8000 msec, inversion time = 2400 msec, field of view = 240\*240\*170, slice thickness = 1 mm, slice number = 170) and a three-dimensional T1-weighted sequence (1\*1\*1-mm voxel size, echo time = 8.1 msec, repetition time = 3.7 msec, flip angle = 8°, field of view = 240\*240\*170, slice thickness = 1 mm, slice number = 170). A neurologist practitioner (JP) examined each T1 MRI scan to ensure that there was no lesion other than thalamic infarct.

In the same session we obtained multislice T2\*-weighted fMRI images with a gradient echo-planar sequence (8 channel head-coil, 2.4\*2.4\*4 mm voxel size, echo time = 35 msec, repetition time = 2463 msec, flip angle = 90°, field of view = 230 \* 230 mm, slice thickness = 4 mm, slice number = 31, volumes = 200). During the acquisition of the resting-state sequences, all participants were instructed to rest in the scanner with their eyes closed.

## 2.4. Lesion localisation in the thalamus - volumetric MTT and hippocampus assessment

Two independent examiners (LD and PE) performed a segmentation of each patient's thalamic lesion with a good inter-rater agreement (κ = .82), using MRIcron software. We used the Krauth version of the Morel atlas of the thalamus to

automatically localise the lesions and quantify (mm³) nuclei damage (Morel, 2007, Krauth et al., 2010, and see Fig. 1a and Danet et al., 2015 for first publication of these localisation results). White-matter lesions were quantified on the Flair sequences using the Fazekas and Schmidt score by two independent raters (LD and MP) (Kapeller et al., 2003).

We used two methods to assess MTT integrity: the MTT label in Morel's atlas and a volumetric analysis based on MTT manual segmentation performed in patients and controls (see Danet et al., 2015 for details about the localisation methods). Finally, patients were grouped in the damaged MTT subgroup (dMTT) if at least one of the two methods indicated damage, and in the intact MTT subgroup (iMTT) if neither method indicated damage. For the analyses, we computed normalized MTT volumes (mm³) relative to the intracranial volume values.

Due to the close proximity and integration of the MTT and hippocampus in the Papez circuit, we sought to reinforce the robustness of the MTT results by computing automatic segmentation of the hippocampus using the Freesurfer software (Segmentation of hippocampal subfields) (Catani et al., 2013; Iglesias et al., 2015). To conduct subsequent analyses, we normalised hippocampus native volumes (left and right whole hippocampus) with the intracranial volume of each subject.

## 2.5. Preprocessing

The SPM (Statistical and Parametric Mapping) software version 12 (Wellcome Department of Cognitive Neurology, University College London, London, England), implemented in Matlab (Mathworks Inc, Sherborn, Massachusetts) was used for spatial and temporal preprocessing. We used the Conn toolbox (http://www.nitrc.org/projects/conn), implemented in Matlab for connectivity analyses (Whitfield-Gabrieli & Nieto-Castanon, 2012). Preprocessing included discarding the first 5 volumes, rigid body motion correction (6 degrees of freedom), slice timing correction by interpolation (reference slice = 16), normalization to the EPI template Montreal Neurologic Institute standard space, and smoothing with an 8-mm full width at half maximum isotropic Gaussian filter. We

<figure><img src="image_5.png" /><figcaption>Fig. 1 – a. Overlap of the patients' lesions (from 1 = blue to 6 = red overlapped lesions, N = 12) on an axial view. A slice of the Morel atlas with structures of interest overlaid (already published in Danet et al., 2015). b. Patients' lesions (from 1 = yellow to 6 = red overlapped lesions, N = 12) and the bilateral Thalamus ROI of the dDMN (in light blue) were overlapped. c. The overlapped part between lesions and thalamus ROI (in dark) was out of the analyses. PuT: putamen, GPe: external globus pallidus, ic: internal capsule, R: reticular nucleus, mtt: mamillothalamic tract, CeM: central medial, CM: centre median, CL: central later al, Hb: habenula, MD: mediodorsal (R: Right, L: Left)</figcaption></figure>
then conducted additional corrections [noise reduction as physiological signal, movement, temporal covariates, temporal filtering and windowing of the blood oxygen level-dependent (BOLD) contrast signal] according to the Conn toolbox default preprocessing procedure (Whitfield-Gabrieli & Nieto-Castanon, 2012).

## 2.6. Network-method based analyses

Following preprocessing, ROI-to-ROI connectivity (RRC) matrices were estimated characterizing the functional connectivity between each pair of regions among each ROI of each network. Functional connectivity strength was represented by Fisher-transformed bivariate correlation coefficients from a general linear model, estimated separately for each pair of ROIs, characterizing the association between their BOLD signal timeseries. We used the Willard–Stanford atlas to identify functional networks (Altmann et al., 2015). Among all the functional networks of Willard–Stanford atlas, we chose networks in which the thalamus is represented as well as networks known to be related to thalamus cognitive functions (i.e., executive functions, memory and language): ventral and dorsal default mode network (vDMN and dDMN) salience network, posterior salience network, left and right executive network, and language network (cf. Figure e-2 in supplementary data). Then, we used the AAL atlas to anatomically label regions belonging to the Willard–Stanford atlas' functional networks. It is important to note that the dDMN has a unique thalamic ROI composed of part of the left thalamus and part of the right thalamus (Fig. 1 light blue). Inside each network, we performed intragroup ROI-to-ROI analyses and second-level analyses using connectivity matrix. Note that, for dDMN that includes a thalamic ROI, we excluded from the analyses any voxel located in a mask gathering all patients' lesions, for both patients and controls (cf. Fig. 1b and c). All the analysis codes used correspond to the codes implemented by default in the Conn software and are available on demand by contacting the corresponding author.

## 2.7. Statistical analyses for imaging data

We tested the difference between groups for each network using network-based statistics (Zalesky et al., 2010) involving permutation tests. Statistically significant differences between groups on connectivity were identified at the connection level ($p < .05$), with correction for multiple comparisons at seed level (FDR $p < .05$) by intensity (NBS, same method previously used in Péran et al., 2021).

## 2.8. Cognitive assessment

All participants underwent a comprehensive neuropsychological assessment. Verbal memory was tested using the Free and Cued Selective Reminding Test (FCSRT), Van der Linden, 2004. The test procedure was as follows: first, subjects were asked to learn 16 words. During this initial learning phase, each word was associated with another word corresponding to a related semantic category. Then, subjects had to recall the words (freely, then giving the related semantic category as a clue for the non-recalled words), 3 times. After that, subjects had to

recognize the 16 words among distractors ($n = 32$). Finally, after a 20 min delay, subjects had to recall the words (free then cued recall). Visual memory (Rey–Osterrieth Complex Figure, Rey and Vaivre-Douret, 1960; DMS48, Barbeau et al., 2004), executive functions (Digit and Spatial Span, Wechsler, 2001; d2 test, Brickenkamp and Zilmer, 1998; Trail Making Test, Godefroy & GREFEX, 2008; Stroop test, Godefroy & GREFEX, 2008; Digit–Symbol test, Wechsler, 1997), language (literal and semantic lexical fluency, Godefroy & GREFEX, 2008), and affect (State-Trait Anxiety Inventory, Spielberger, 1983; Starkstein Apathy Scale, Starkstein and Leentjens, 2008; Beck Depression Inventory, Beck and Steer, 1993) were also tested. Handedness was assessed with the Edinburgh Handedness Inventory (Oldfield, 1971). The neuropsychological tests used in this study are used in everyday clinical practice and are subject to copyright. Collected cognitive raw data are not publicly available due to legal and ethical barrier (for the re-use of the data a new ethical agreement is necessary).

## 2.9. Statistical analyses for cognitive data

The group comparisons were carried out using $X^2$, the nonparametric Mann–Whitney U test, and permutation tests (Ernst, 2004). Hedge's *g* was used to compute effect size where $g < .2$ represents a small effect, $.2 < g < .8$ represents a medium effect and $g > .8$ represents a large effect. Correlation analyses were performed on cognitive variables whose group-level comparisons reached statistical significance ($p < .05$). We first conducted correlations between cognitive performance and FC in patients and controls separately. Then, when there were no significant results from the analysis using the separated groups (i.e., patients/controls), in order to increase statistical power we pooled controls and patients together to assess the relationships between cognitive processes, volumetry (lesions, MTT, hippocampus) and FC using a Spearman rho test. In order to better understand observed significant relationships, we drove a mediation analysis. Multiple comparisons were corrected using the Bonferroni-Holm correction (Holm, 1979). Modified kappa scores were calculated to assess inter-observer reproducibility for the Fazekas and Schmidt score volumes of the segmented lesions (Chakravarty et al., 2009).

# 3. Results

## 3.1. Participants

We recruited 12 patients and 12 healthy controls matched to the patients for age and education level (See Table 1 for demographic data for both groups).

## 3.2. Localization of lesions - MTT and hippocampus volumes

Lesions outside the thalamus (averaged % of normalized outside lesion volume: 13.7%, st. dev: 24.5%) were not in critical regions for cognition according to atlas analyses based on the T1 sequences (see Figure e-1 in the supplementary data for the native patients' T1 sequences). Damage to the medial
<table><caption>Table 1 – Demographic data of patients and controls. Mean (standard deviation) [min, max]. Mann–Whitney, permutations and X² tests were used for the comparisons.</caption><thead><tr><th></th><th>Left thalamic<br>infarct patients<br>(n = 12)</th><th>Healthy control<br>subjects<br>(n = 12)</th><th>p<br>value</th><th>dMTT group<br>(n = 7)</th><th>iMTT group<br>(n = 5)</th><th>p<br>value</th></tr></thead><tbody><tr><td>Age</td><td>53.2 (14.6) [25, 75]</td><td>52.7 (13.2) [25, 69]</td><td>.94</td><td>58.9 (16.6) [25, 75]</td><td>45.2 (6.3) [38, 52]</td><td>.37</td></tr><tr><td>Gender</td><td>3F/9M</td><td>9F/3M</td><td>.01</td><td>1F/6M</td><td>2F/3M</td><td>.31</td></tr><tr><td>School level (years)</td><td>12.8 (4.1) [5, 17]</td><td>13.9 (4.4) [5, 19]</td><td>.24</td><td>12.3 (4.2) [5, 17]</td><td>11 (4.2) [5, 17]</td><td>.69</td></tr><tr><td>Handedness<br>(Edinburgh scale)</td><td>11 R/1L</td><td>11R/1L</td><td>1</td><td>6 R/1A</td><td>5 R</td><td>.38</td></tr><tr><td>Time gap since<br>stroke onset</td><td>589 days (588.9)<br>[3 months,<br>4 years 11 months]</td><td>—</td><td>—</td><td>527 (647.2) days<br>[3 months,<br>4 years 11 months]</td><td>675 (556.1) days<br>[3 months,<br>3 years 8 months]</td><td>.81</td></tr><tr><td>Normalized volume<br>of the lesions (mm³)</td><td>516.8 (265.2) [30, 982]</td><td>—</td><td>—</td><td>679.6 (160.7) [538, 982]</td><td>289 (208.5) [30, 605]</td><td>.64</td></tr></tbody></table>

group was observed in all patients, especially in the medi- odorsal nucleus (MD. averaged % of MD theoretical volume: 13.2, st. dev: 10.6) (n = 11) and in the intralaminar nuclei (averaged % of intralaminar nuclei theoretical volume: 8.6, st.dev: 8.8) (n = 12). No lesions were found in the anterior nucleus. Localization of the lesions using the Morel atlas and the MTT volumetric analysis revealed MTT damage in 7 pa- tients exhibiting severe atrophy of the left MTT (z scores < -3.2DS). Damaged and intact MTT patients (respec- tively dMTT and iMTT patients) showed a group-level differ- ence for the left but not the right MTT (respectively *p* < .05 and *p* > .1). We also noticed lesions in the lateral group for 10 pa- tients. Note that for the lesion localization, results have already been published elsewhere (Danet et al., 2015). Overlap in the patients' lesions are showed in Fig. 1a.

No difference was found between patients and controls, nor between dMTT and iMTT patients regarding hippocampus volume. However, a correlation between the hippocampus and the MTT was found in the left hemisphere of patients (whole hippocampus: rho = .6, p uncorr<.05; pcorr = ns).

### 3.3. Group comparisons in functional connectivity results

#### 3.3.1. Controls versus patients

For the patients' group, compared with controls: the dDMN- related map showed a significant decreased functional con- nectivity between the remaining thalamus region and left and right superior medial frontal, left and right anterior cingulum, and left and right medial orbitofrontal cortices (cf. Fig. 2a and Table 2, and see Figure e-3 in supplementary data the distri- bution of the individual dDMN FC across patients and con- trols). For more anatomical details regarding the significant clusters location please look at the Figure e-4 in the supple- mentary data.

Also, the vDMN-related map demonstrated a significant decreased functional connectivity between the right middle frontal region and left calcarine, right lingual and right pre- cuneus, left and right middle occipital, and left fusiform cortices (cf. Fig. 2b and Table 2, and see Figure e-3 in sup- plementary data the distribution of the individual vDMN FC across patients and controls). For more anatomical details regarding the significant clusters location please look at the Figure e-4 in the supplementary data.

Finally, the salience posterior-related map displayed a sig- nificant uncorrected decreased functional connectivity be- tween the right middle cingulum and left and right supramarginal, right postcentral and right superior parietal cortices (cf. Table 2).

We found no difference in FC between patients and con- trols for the salience, executive left, executive right and language-related matrix.

#### 3.3.2. iMTT versus dMTT patients

We found no FC difference between the 2 subgroups.

### 3.4. Groups comparisons in cognitive assessment

#### 3.4.1. Patients versus controls

Patients performed lower than controls in verbal memory recall and recognition in the FCSRT (g values:8–2.05. See Figure e-3 in supplementary data to look at the distribution of the individual FCSRT Free and cued recall across patients and controls), whereas we found no difference in visual memory (see Table 3). We observed a dysexecutive impairment involving response time and executive tasks in the patients' group. Also, the patients displayed impaired semantic (g = 2.84) and literal (g = 1.56) fluencies. There was no differ- ence between the groups in affect scales.

#### 3.4.2. Performance of dMTT and iMTT patients

dMTT patients were severely impaired on all verbal memory tasks, compared with both controls and iMTT patients (de- mographic data in Table 1; and details in Table e-1 in sup- plemental data). Also, they had significantly lower scores than controls in semantic fluency, verbal working memory (digit span) and selective attention (d2). By contrast, the iMTT pa- tients were only impaired on the semantic fluency, perform- ing worse than controls. The dMTT and iMTT subgroups did not differ significantly from controls on the rest of the assessment.

### 3.5. Correlation analyses

#### 3.5.1. Correlations between abnormally connected network and cognitive results

We drove correlations analyses between all the significantly different cognitive indices and FC. There were no significant
<figure><img src="image_6.png" /><figcaption>Fig. 2 – Functional connectivity results. On the left, the graphs display only the significant intergroup comparisons. These are marked in the figures on the right by a star attached to the ROI concerned. a. The FC within the dDMN network between patients and controls [controls > patients, threshold ROI-to-ROI connections (by intensity): .05, p uncorr., threshold seed ROIs (by size): .05, p FDR corrected]. We averaged FC indices across the significant comparisons because the patients always had lower FC than the controls. b. The FC within the vDMN network between patients and controls [controls > patients, threshold ROI-to-ROI connections (by intensity): .05, p uncorr., threshold seed ROIs (by size): .05, p FDR corrected]. We averaged FC indices across the significant comparisons because the patients always had lower FC than controls. (R: Right, L: Left).</figcaption></figure>
<table><caption>**Table 2** – Significant results of the ROI-to-ROI FC analysis in the selected networks. t tests were used to compare the FC matrix.</caption><thead><tr><th></th><th>ROI to</th><th>ROI</th><th>T</th><th>P UNCORR</th><th>P FDR</th></tr></thead><tbody><tr><td rowspan="6">vDMN</td><td rowspan="6">Frontal_Mid_R_2</td><td>Calcarine_L</td><td>5.64</td><td>.0000</td><td>.0001</td></tr><tr><td>Precuneus_R_Lingual_R</td><td>4.67</td><td>.0001</td><td>.0007</td></tr><tr><td>Occipital_Mid_R</td><td>4.36</td><td>.0003</td><td>.0010</td></tr><tr><td>Occipital_Mid_L</td><td>3.92</td><td>.0007</td><td>.0022</td></tr><tr><td>Fusiform_L</td><td>2.69</td><td>.0134</td><td>.0321</td></tr><tr><td>Precuneus_R_2</td><td>2.56</td><td>.0177</td><td>.0353</td></tr><tr><td rowspan="10">dDMN (lesion masked)</td><td rowspan="10">Thalamus_L_R</td><td>Frontal_Sup_Medial_L_2</td><td>3.42</td><td>.0025</td><td>.0255</td></tr><tr><td>Cingulum_Ant_L_2</td><td>3.36</td><td>.0029</td><td>.0255</td></tr><tr><td>Frontal_Sup_Medial_L_R</td><td>3.23</td><td>.0038</td><td>.0255</td></tr><tr><td>Cingulum_Ant_L_3</td><td>3.05</td><td>.0059</td><td>.0273</td></tr><tr><td>Frontal_Med_Orb_R</td><td>2.92</td><td>.0079</td><td>.0273</td></tr><tr><td>Cingulum_Ant_L_R</td><td>2.91</td><td>.0082</td><td>.0273</td></tr><tr><td>Frontal_Med_Orb_L</td><td>2.80</td><td>.0105</td><td>.0299</td></tr><tr><td>Frontal_Sup_Medial_L</td><td>2.65</td><td>.0145</td><td>.0268</td></tr><tr><td rowspan="5">Salience post</td><td rowspan="5">Cingulum_Mid_R</td><td>Supramarginal_R</td><td>-2.68</td><td>.0135</td><td>.1099</td></tr><tr><td>Supramarginal_L</td><td>-2.31</td><td>.0305</td><td>.1099</td></tr><tr><td>Postcentral_R</td><td>-2.18</td><td>.0405</td><td>.1099</td></tr><tr><td>Parietal_Sup_R</td><td>-2.12</td><td>.0453</td><td>.1099</td></tr><tr><td>Supramarginal_R_2</td><td>-2.12</td><td>.0458</td><td>.1099</td></tr></tbody></table>

<table><caption>**Table 3** – Results of the cognitive assessment. Median [min, max]. Mann-Whitney test was used to compare the groups. Hedge's g was used to compute effect size where g < .2 means small effect, .2 < g < .8 means medium effect and g > .8 means large effect.</caption><thead><tr><th>Tasks</th><th>Subtests</th><th>Left thalamic<br>infarct patients<br>(n = 12)</th><th>Healthy<br>controls<br>(n = 12)</th><th>P value</th><th>g Hedge</th></tr></thead><tbody><tr><td><strong>Memory</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td rowspan="4">Free and cued selective<br>reminding test - verbal</td><td>Immediate free + cued recall/48</td><td>29 [7, 48]</td><td>48 [45, 48]</td><td>&lt;.001</td><td>1.66</td></tr><tr><td>Delayed free recall/16</td><td>5 [0, 13]</td><td>15 [10, 16]</td><td>&lt;.0001</td><td>2.05</td></tr><tr><td>Delayed free + cued recall/16</td><td>10 [1, 16]</td><td>16 [15, 16]</td><td>&lt;.05</td><td>1.37</td></tr><tr><td>Recognition/48</td><td>45 [5, 48]</td><td>48 [47, 48]</td><td>&lt;.01</td><td>.8</td></tr><tr><td>Rey figure - visual</td><td>Delayed recall (2 min)/36</td><td>19.8 [3, 32]</td><td>24 [17, 34]</td><td>ns</td><td></td></tr><tr><td>DMS 48 - visual</td><td>Delayed forced-choice<br>Recognition (60 min)/48</td><td>47.5 [44, 48]</td><td>47 [38, 48]</td><td>ns</td><td></td></tr><tr><td><strong>Executive functions</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Auditory-verbal span</td><td>Succeeded items</td><td>14 [8, 21]</td><td>21 [15, 25]</td><td>&lt;.01</td><td>1.41</td></tr><tr><td>Visuospatial span (n = 11)</td><td>Succeeded items</td><td>16 [11, 26]</td><td>18 [14, 23]</td><td>ns</td><td></td></tr><tr><td>Digit symbol</td><td>Items in 2 mn</td><td>58 [36, 88]</td><td>79.5 [58, 108]</td><td>&lt;.05</td><td>1.2</td></tr><tr><td>Stroop</td><td>Response time</td><td>77 [30, 150]</td><td>37.5 [5, 88]</td><td>&lt;.05</td><td>1.05</td></tr><tr><td>Trail making test</td><td>Response time</td><td>54 [27, 194]</td><td>38.5 [8, 97]</td><td>ns</td><td></td></tr><tr><td>Selective attention (d2)</td><td>Rhythm</td><td>277 [215, 436]</td><td>437.5 [304, 549]</td><td>&lt;.001</td><td>1.69</td></tr><tr><td><strong>Language</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Literal fluency (p)</td><td>Number of words in 2 min</td><td>15.5 [8, 23]</td><td>22 [17, 37]</td><td>&lt;.001</td><td>1.56</td></tr><tr><td>Semantic fluency (animals)</td><td>Number of words in 2 min</td><td>22.5 [16, 40]</td><td>43.5 [35, 55]</td><td>&lt;.0001</td><td>2.84</td></tr><tr><td><strong>Behaviour</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Starkstein apathy scale/42</td><td></td><td>9.5 [0, 18]</td><td>8 [0, 17]</td><td>ns</td><td></td></tr><tr><td>Beck depression inventory</td><td></td><td>3 [0, 8]</td><td>3 [0, 13]</td><td>ns</td><td></td></tr><tr><td>State-trait anxiety/80</td><td></td><td>38 [28, 51]</td><td>38.5 [24, 57]</td><td>ns</td><td></td></tr></tbody></table>

correlations between cognitive performance and FC in patients nor controls separately. When we pooled together patients and controls, we found significant correlations between FC (mean r-to-z transformed in one network) and the results for memory assessed by FCSRT (cf. Fig. 3) and verbal fluencies only. The 3 free recalls were significantly correlated with FC in dDMN and in vDMN (FC mean in dDMN/3 free recalls: rho = .63, p = .0009,

pcorr = .01; FC mean in vDMN/3 free recalls: rho = .59, p = .003, pcorr = .02). In the 3 cued recalls, we did not conduct the correlation analysis because there was a ceiling effect in the subjects' performance. Also, the delayed free recall was significantly correlated with FC in dDMN and in vDMN (FC mean in dDMN/delayed free recall: rho = .61, p = .002, pcorr = .02; FC mean in vDMN/delayed free recall: rho = .63, p = .001, pcorr = .01).
<figure><img src="image_7.png" /><figcaption>Fig. 3 – Correlations between abnormally connected networks and cognitive results. Top: correlations between FC in dDMN and memory performance (3 free recalls, delayed free recall) and semantic fluency. Bottom: correlations between FC in vDMN and memory performance (3 free recalls, delayed free recall) and verbal fluencies (semantic and literal).</figcaption></figure>

In addition, between semantic fluency and FC (cf. Fig. 3), we found a positive correlation with dDMN and vDMN (FC mean in dDMN/semantic fluency: $rho = .58, p = .003$, pcorr = .03; FC mean in vDMN/semantic fluency: $rho = .75, p = .00002$, pcorr = .0002). Finally, we found that literal fluency was correlated with vDMN FC (FC mean in vDMN/literal fluency: $rho = .59, p = .002$, pcorr = .02, cf. Fig. 3).

### 3.5.2. Correlations between abnormally connected networks and thalamic lesion volume, MD lesion volume, MTT volume as well as hippocampus volume

We observed no significant correlation between FC and thalamic lesion volume (FC mean in dDMN/thalamic lesion: $rho = .15, p = .7$; FC mean in vDMN/thalamic lesion: $rho = -.38, p = .2$). We observed no significant correlation between FC and MD lesion volume (FC mean in dDMN/MD lesion: $rho = .22, p = .5$; FC mean in vDMN/MD lesion: $rho = -.38, p = .2$).

dDMN mean FC was significantly correlated with normalized left MTT volume (FC mean in dDMN/left MTT volume: $rho = .59, p = .003$, pcorr = .03; FC mean in vDMN/left MTT volume: $rho = .3, p = .2$) (Cf. Fig. 4). Although the analyses do not stand up to correction for multiple comparisons, it should be noted that, in patients, normalized left MTT volume was significantly correlated with left whole hippocampus ($rho = .6, p$ uncorr$<.05$; pcorr = ns). This was not found in controls (left MTT volume/left whole hippocampus volume: $rho = .36, p = .2$) nor with right MTT (right MTT volume/right whole hippocampus volume. In patients: $rho = .49, p = .1$; in controls: $rho = .38; p = .2$). No correlations were found between vDMN FC and left MTT volume (FC vDMN/left MTT: in patients $rho = -.2, p = .6$; in controls $rho = -.1, p = .7$). We found no significant correlation between FC and left or right hippocampus volume in patients (FC mean in dDMN/left whole hippocampus vol. $rho = .27, p = .4$; FC mean in vDMN/left

<figure><img src="image_8.png" /><figcaption>Fig. 4 – Correlation between FC in dDMN and MTT normalized volume.</figcaption></figure>

whole hippocampus vol. $rho = -.1, p = .8$; FC mean in dDMN/right whole hippocampus vol. $rho = .19, p = .6$; FC mean in vDMN/right whole hippocampus vol. $rho = -.24, p = .5$; FC mean in vDMN/left whole hippocampus vol. $rho = -.4, p = .2$; FC mean in dDMN/right whole hippocampus vol. $rho = -.04, p = .9$; FC mean in vDMN/right whole hippocampus vol. $rho = -.38, p = .2$).

To go further and try to understand these relationships between MTT, MD and memory we drove a mediation analysis. The findings showed significant results with Left MTT as a mediator of i/the link between FC dDMN and 3 free recalls (Estimate 33.9 [CI 11.35–56.4] Z = 2.95 $p = .003$, pcorr = .02) ii/ the link between FC dDMN and delayed free recall (Estimate 15.78 [CI 6.08–25.5] Z = 3.19 $p = .001$, pcorr = .008) (see Table e-2 in supplementary data for more detailed results.
## 4. Discussion

Our results showed FC decreased functional connectivity in the patient group compared with controls in both the dDMN and vDMN networks. We also found impairment on verbal memory and executive tasks, including verbal fluency, in patients compared with controls. Comparing the dMTT and iMTT groups together, we showed that dMTT were more impaired in verbal memory tasks, semantic fluency, verbal working memory and selective attention. Correlation analyses pooling data from patients and healthy controls showed that verbal memory performance and semantic fluency were positively correlated with FC in dDMN and vDMN. Literal fluency was only positively correlated with FC in vDMN. A significant correlation was found between FC in dDMN and left MTT volume. Finally, a mediation analysis showed that this is the left MTT which mediates the relationship between FC in the DMN and memory.

In the present study, we have shown that FC is significantly altered in the DMN and that FC in the DMN was correlated with memory performance. It is important to note that dDMN was the only ROI with damage in the thalamic area presented by our patients. From a neuropsychological point of view, how might DMN activity and memory impairment be related?

Among the regions disconnected from the DMN, frontal regions were largely represented. Historically, the PFC has been anatomically delineated by structural connections with the MD (Uylings et al., 2003). Due to these existing dense thalamofrontal connections, the observed functional thalamofrontal functional change following medial thalamic damage could be due to a deafferentation mechanism (Alves et al., 2019; Greicius et al., 2009). Some authors postulated that the MD plays a role of executive supervisor, acting as the cornerstone of a temporal-MD-prefrontal system of memory and, more generally, cognitive coordination (Mitchell & Chakraborty, 2013). More recently, Pergola et al. proposed that the MD acts as a regulator of PFC activity (Pergola et al., 2018). At this stage, we wanted to find out whether the memory impairment was related to secondary DMN functional change rather than medial thalamic damage per se. However, we have to be very cautious about our findings, especially the correlation analyses involving FC, because of the risk of over-interpretation and because we pooled the data from patients and controls to increase statistical power.

The role of the DMN in the introspective part of retrieval has already been demonstrated (Buckner and Carroll, 2007; Buckner et al., 2008). In particular, the Cascaded Memory Systems (CMS) model proposes that there is an important link between retrieval and the DMN, supported by anatomical, functional and theoretical arguments (Kaefer et al., 2022). In this model, the DMN provides the backbone for the propagation of replay, mediating interactions between the hippocampus and neocortex that enable the consolidation of new memories. The DMN may also independently trigger replay cascades that support the reactivation of older memories or high-level semantic representations. Theoretically, this could explain our results for both memory performance and semantic fluency, although the mnemonic nature of the underlying cognitive mechanism remains uncertain. Indeed,

metamemory may be another candidate. We did not specifically assess self-projection and metacognition in our study, but we know that these functions are supported by the DMN and are required for episodic memory retrieval (Wen et al., 2020). Focusing in particular on the cognitive involvement of the PFC and anterior cingulate cortex (ACC), which are part of the DMN, some authors have proposed that the role of the PFC in episodic retrieval actually reflects monitoring, memory trials (i.e., the search for a memory itself) and autonomic awareness (Wen et al., 2020; Wheeler et al., 1997; Yonelinas, 2002). The PFC is also thought to be involved in reconstructive mnemonic processes and self-referential processes such as confidence judgments, which appear to depend on the ACC as well (Buckner & Carroll, 2007; Chua et al., 2006; Moritz et al., 2006; Svoboda et al., 2006). For example, Chua et al. used a face-name associative fMRI task to investigate recognition memory and confidence (Chua et al., 2006) and showed that activation in the anterior and posterior cingulate, medial prefrontal and medial temporal lobes varied with confidence level, with high confidence judgments associated with high activation in these areas. Some authors specified which parts of the PFC would be involved in such processes. For instance, examining the activity of the DMN during a set of social cognition and memory tasks, Wen et al. found that the theory of mind and moral dilemmas tasks showed strong activation of dorsal Medial PFC, while the autobiographical memory and spatial imagery tasks showed peaks of activation in ventral Medial PFC (Wen et al., 2020). Moreover, Baird et al. found that metacognitive accuracy, as assessed by confidence judgments associated with memory retrieval, was significantly correlated with FC between the right medial PFC and two clusters in the right parietal cortex (Baird et al., 2013). Thus, although it seems that many studies have identified the PFC, cingulate cortices and DMN as the main candidates for self-projection, there are no clear results confirming a specific and dedicated involvement in memory (retrieval) or metamemory (memory judgement).

We have just discussed how cognitive impairment in our patients could be specifically related to reduced FC in the DMN. A specific relationship between the thalamus (and even MD) and FC in the DMN has already been shown in healthy subjects or in other pathologies, and we expected to find convergent results (Cunningham et al., 2017; He et al., 2015; Jones et al., 2011). However, given that FC in the DMN was not correlated with MD lesion volume, we wonder whether the decrease in FC does not reflect a reorganisation effect due to the stroke (all patients in the present study were in the chronic phase), independent of the thalamic location (Dacosta-Aguayo et al., 2015; Tuladhar et al., 2013). For example, in a longitudinal study, Veldsman and colleagues showed increasing atrophy of DMN regions over time (measured at 3 months and 1 year after stroke onset) in stroke patients with cognitive impairment. The authors propose that this cognitive impairment is due to post-stroke degeneration independently of lesion location (Veldman et al., 2018). However, the fact that the observed decreased functional connectivity was not correlated with total lesion volume in our study seems to invalidate this last possible explanation. Thus, the postulate of a possible modification of intrathalamic connectivity as a consequence of the medial thalamic stroke
affecting the DMN might be weak, because we do not really
know what is related to the lesion effect and what to the post-
lesion reorganisation. The interindividual variability con-
cerning post-stroke reorganisation could motivate to conduct
a similar study with a multi-case method where each patient
would be compared with a template from a matched controls
group. Besides, there are also theoretical uncertainties
regarding the correspondence between structural and func-
tional connectivity, as well as between task-dependent and
resting-state functional MRI findings (Horwitz et al., 2013).

Finally, perhaps the key point in our findings is that
although thalamic lesion volume and MD lesion volume did
not explain the observed functional change, MTT volume
appeared to be positively correlated with FC in the dDMN.
Furthermore MTT mediates the relationship between FC in
the DMN and memory. What does that mean?

The more relevant hypothesis may be that the lesion in the MTT (7 patients affected) affect FC in the DMN and may induce compensatory rearrangements in hippocampal substructures leading to memory impairment. Therefore, the MD lesion per se would not have a direct effect on FC in the DMN. Furthermore, we have shown here that memory performance appears to be positively correlated with FC in the DMN, whereas in a previous study we reported a positive correlation between MTT volume and memory performance (Danet et al., 2015). Thus, if we consider that MTT belongs to the structural DMN, our results, especially those showing that contrary to MD, MTT is a mediator of FC DMN/memory relationship, suggest that cognitive and memory deficits and DMN functional change may reflect a direct effect of MTT lesions rather than an indirect effect of MD lesions (Alves et al., 2019).

**4.1. Limitations**

Due to the difficulty in recruiting such patients (with pure
isolated thalamic stroke), some characteristics of our sample
show a certain heterogeneity, especially with regard to the
time interval after onset. It would also have been interesting
to monitor cognitive complaints and compare them with
lesion volume, FC results and cognitive performance. For
future research, we recommend focusing on the MD when
recruiting patients (MD lesion should be an inclusion crite-
rion), assessing executive functions more comprehensively,
and investigating the FC between the theoretical MD and the
rest of the brain in healthy participants. To achieve this last
point, there are some obstacles to overcome in terms of voxel
size in rsfMRI acquisitions and the atlas used.

CRediT authorship contribution statement

Lola Danet: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Emmanuel J. Barbeau: Writing – review & editing, Validation, Supervision, Conceptualization. Marie Lafuma: Conceptualization. Fabrice Bonneville: Conceptualization. Igor Sibon: Resources. Jean-François Albucher: Writing – review & editing, Resources. Jérémie Pariente: Writing – review & editing,

Validation, Supervision, Methodology, Investigation, Concep-
tualization. **Patrice Peran:** Writing – review & editing, Vali-
dation, Supervision, Project administration, Methodology,
Investigation, Conceptualization.

Conflict of interest

No conflicts of interest.

Acknowledgements

We would like to thank Mélanie Planton for double rating the
Fazekas and Schmidt scores and Pierre Eustache for the dou-
ble manual segmentations of the MTT and lesions. We thank
the INSERM/UPS UMR1214 Technical Platform for performing
the MRI scans.

Supplementary data

Supplementary data to this article can be found online at
https://doi.org/10.1016/j.cortex.2024.10.024.

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