Abnormal Degree Centrality and Functional Connectivity Associated with Cognitive Impairment in Myotonic Dystrophy Type 1

doi:10.21203/rs.3.rs-5299264/v1

Objectives: The present study aimed to examine alterations in voxel-based degree centrality (DC) and functional connectivity (FC), and their relationship with cognitive impairments in individuals with myotonic dystrophy type 1 (DM1).

Methods: Eighteen DM1 patients and eighteen healthy controls (HCs) participated in the study and were assessed using a comprehensive neuropsychological battery. Voxel-wise DC analysis was conducted to identify abnormal neural hubs in DM1 patients. Additionally, FC method was used to assess abnormalities in functional connections among these aberrant hubs. Correlational analyses were also used to identify and explore the relationship between altered DC and FC values and cognitive performance in DM1 patients.

Results: DM1 patients exhibited reduced DC in the bilateral Rolandic operculum, left inferior frontal gyrus (triangular part), right angular gyrus, right median cingulate and paracingulate gyri, and right middle temporal gyrus. Conversely, increased DC was observed in the right fusiform gyrus, right hippocampus and left inferior temporal gyrus. FC analysis revealed that altered connectivity predominantly occurred between the right middle temporal gyrus, right angular gyrus and left inferior frontal gyrus (triangular part). Notably, the DCvaluein the right median cingulate was positively correlated withthe Trail Making Test Part A scores in DM1 patients (r = 0.616, p = 0.005, adjusted p <0.05). No significant correlations were discovered between FC values and neurocognitive performances.

Conclusion: The study demonstrated that abnormalities in degree centrality and functional connectivity may become potential neuroimaging biomarkers for cognitive decline in DM1 patients.

Resting-state functional MRI

Myotonic Dystrophy Type 1

Degree Centrality

Functional Connectivity

Cognitive Dysfunction

Myotonic dystrophy type 1 (DM1) has become the most common muscular dystrophy, exhibiting a prevalence rate varying from 0.5 to 18.1 per 100,000 population (Theadom et al., 2014). This progressive multisystem disease, associated with trinucleotide repeat expansion of CTG (cytosine-thymine-guanine) located in the DMPK (dystrophia myotonica protein kinase) gene, is inherited and intensifies across generations(Theadom et al., 2014). DM1 manifests at various ages but is typically identified in adulthood, primarily affecting muscular functions. However, a substantial proportion of patients also experience a broad spectrum of multisystemic symptoms including apathy, sleep disturbances, behavioral disorders, and cognitive impairments (Okkersen et al., 2017; Theadom et al., 2014). Previous neuropsychological assessments have confirmed that the pervasive cognitive decline across multiple domains in DM1 patients, affecting areas such as memory, language, visuospatial abilities and executive function (Okkersen et al., 2017).

Recent advances in neuroimaging have begun to elucidate brain structural or neuronal functional activity changes in individuals with DM1. Our prior research (Huang et al., 2021), combined various analytic techniques, such as amplitude of low-frequency fluctuations (ALFFs), Wavelet transform-based ALFFs (Wavelet-ALFFs), fractional amplitude of low-frequency fluctuations (fALFFs) and percent amplitude of fluctuation (PerAF) to investigate regional brain characteristics in DM1 patients. The study revealed reduced activity within the executive control networks (ECN) and the default mode (DMN) alongside heightened fluctuations in the cerebellum and temporal cortex. Further, clustering analyses have indicated increased changes in white matter functional networks (Li et al., 2022). While these studies have a significant impact for our understanding of DM1-related brain dysfunction, the direct correlations between these neuroimaging anomalies and cognitive decline remain to be clarified.

Degree centrality (DC) (Zuo et al., 2012), a graph theoretical measure, quantifies the functional relationships between a particular brain region and all other regions within the brain’s entire connectivity matrix at the voxel level. This method effectively maps the whole brain functional connectivity network and identifies aberrant hubs, providing valuable insights into the underlying network alterations observed in various neurological disorders (Guo et al., 2016; Liao et al., 2021; Zhang et al., 2024). Although DC analysis has demonstrated high sensitivity, specificity, and reliability(Zuo & Xing, 2014), its application in myotonic dystrophy type 1 (DM1) remains limited.

Functional Connectivity (FC) of the human brain is the term used to describe the temporal relationship between neuronal activity patterns across brain regions that are separated anatomically, elucidating how different areas of the brain interact during both disease and normal health states (Friston et al., 1993). Resting state functional MRI (rs-fMRI) has become a pivotal method in characterizing these large-scale functional networks (Kelly & Castellanos, 2014; Lowe et al., 2000).

The main objective of the current study was to explore aberrant neural patterns in DM1 patients using voxel-wise DC and FC analyses. Additionally, this research aimed to understand and explore the associations between alterations in DC and FC and cognitive performance in DM1 patients, thereby exploring potential neuroimaging biomarkers for cognitive decline in this population.

Participants

The diagnosis of DM1 was confirmed through clinical disease symptoms and electromyographic evidence, with genetic verification for all cases. Eighteen DM1 patients were recruited alongside eighteen demographically well-matched healthy controls (HCs) in the aspect of age, sex and average education years. Inclusion criteria in this study included right-handedness as determined by the Edinburgh Handedness Inventory (Oldfield, 1971), age between 20 and 80 years, and no record of significant neurological or psychiatric disorders other than DM1. Exclusion criteria comprised a history of substance abuse, significant head trauma or stroke, other neuromuscular disorders, and MRI contraindications. All participants provided an informed consent letter. After approval by the Ethical Committee of the Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, the research plan was registered on the Chinese Clinical Trial Registry (ChiCTR2000032978).

Clinical and Cognitive Assessments

Neurological evaluations were conducted by experienced neurologists. The Mini-Mental State Examination (MMSE) was utilized to measure global cognitive abilities. The evaluation of multiple cognitive domains (memory, attention, language skills, executive functions and visuospatial abilities) was conducted through various neuropsychological tests, including the Trail Making Test (TMT, including Part A and B), the Digital Span Test (DST, including the forward and backward count tests), the Auditory Verbal Learning Test (AVLT), the Boston Naming Test (BNT) and the Rey-Osterrieth Complex Figure test (copying version). In our variables, TMT evaluated executive functioning and cognitive processing speed (Sánchez-Cubillo et al., 2009). DST was used as a reliable test to assess working memory capacity and attention (Leung et al., 2011). The AVLT was used as an indicator of the immediate and delayed recall of verbal information (Zhao et al., 2012). The BNT assessed visual confrontation naming ability (Williams et al., 1989) and the copy of ROCF represented visuospatial abilities (Zhang et al., 2021).

Magnetic Resonance Imaging and Data Processing

Participants were given instruction to remain awake while lying still with their eyes closed in the scanner, throughout the imaging session, which was conducted using a Tesla GE Medical System scanner (GE Healthcare, Little Chalfont, United Kingdom). Anatomical images with high-resolution T1-weighting were captured using a fast spoiled gradient-echo (FSPGR) sequence in three dimensions. The scanning parameters for the distinct sequences consisted of a repetition time (TR) of 5.5 milliseconds(ms), an echo time (TE) of 1.7 ms, a flip angle (FA) of 12 degrees, a matrix size of 256 ×256, and a slice thickness of 1 millimeter(mm). For the resting-state functional imaging, an echoplanar imaging (EPI) sequence was utilized. The primary scanning parameters for the EPI sequence were as follows: TR = 2,000 ms, TE = 30 ms, FA = 90°, slice thickness = 4 mm without slice gap, slice number = 35, in-plane matrix= 64 × 64, and scanning time = 420 seconds to yield a total of 210 volumes for each subject.

Resting-State fMRI Data Preprocessing

Following the exclusion of two participants because of excessive head motion, the imaging data of 16 DM1 patients and 18 healthy controls (HCs) were included in the preprocessing analysis. All resting-state fMRI data preprocessing was performed using Statistical Parametric Mapping software (SPM12, www.fil.ion.ucl.ac.uk/spm), Data Processing & Analysis for Brain Imaging (DPABI_V4.1, http://rfmri.org/dpabi), and additional MATLAB scripts (http://mind.huji.ac.il/white-matter.aspx). At the beginning, the structural images, that were co-registered to the functional scans after motion correction, were divided by the New Segment algorithm in SPM12. Using the DARTEL algorithm, the segments were then spatially normalized into Montreal Neurological Institute (MNI) space (Ashburner, 2007).

Functional image preprocessing steps involved several steps: (1) Removal of the first 10 temporal points to allow for signal equilibration. (2) Slice-timing correction to adjust the timing of slice acquisition. (3) Realignment to correct for head movements, with exclusion criteria set at maximum head movements that greater than 2 mm displacement or 2 degrees in any direction. (4) Normalization of the functional images to MNI template with a resolution of 3×3×3 mm³ by means of the DARTEL algorithm. (5) Spatial smoothing using an isotropic Gaussian filter with a full-width at half-maximum (FWHM) of 6 mm. (6) Nuisance regression employing linear trends, a 24-parameter head motion correction model (Friston et al., 1996) and the mean CSF signal as regressors. (7) Temporal filtering, also known as scrubbing, to mitigate the impact of motion spikes, defined as framewise displacement (FD) exceeding 1. (8) Band-pass filtering of the time series data within the 0.01 to 0.08 Hz frequency range to retain frequencies of interest.

Degree Centrality Analysis

In this study, each voxel within the brain was treated as a node within the network, and the degree centrality (DC) of a node was calculated to indicate its ability to connect with all other network nodes, which is crucial for functional integration. The premise is that the greater the node degree, the higher the DC, denoting a node’s significance within the network. The DC analysis was conducted using the DPABI software, which facilitated the examination of the preprocessed fMRI data. Specifically, the correlation among the time series of every voxel and all other voxels was computed to generate a DC map of the entire brain. These correlation coefficients were converted using the Fisher Z transformation to achieve a normal distribution. The whole functional networks of the brain were established by applying a threshold to each correlation coefficient, with values greater than 0.25 considered significant (Buckner et al., 2009; Power et al., 2012; Zuo et al., 2012). Subsequently, a 6 mm × 6 mm × 6 mm FWHM Gaussian kernel was applied to smooth the DC maps. Notably, this smoothing was only applied during the DC mapping phase and not during the initial data preprocessing stage.

To identify significant differences in DC values between groups, an independent t-test for two samples was utilized. The statistical threshold for significance was set at a voxel-wise p-value of less than 0.001 and a cluster-level p-value of less than 0.05, and we used Gaussian Random Field (GRF) correction to control for multiple comparisons (p<0.05).

Functional Connectivity Analysis

Functional connectivity (FC) assessments were performed utilizing the DPABI software, consistent with the analysis software used for DC.

For FC calculations, the preprocessed images underwent additional smoothing with a 6 mm^3 Gaussian kernel, a step distinct from the DC calculations which did not involve smoothing. Initially, based on significant differences in DC between the two study groups, regions of interest (ROIs) were identified and defined. Subsequently, for each ROI, the representative time series was computed using the average fMRI data of all voxels within the ROI for each participant. Functional connectivity was then measured using the Pearson correlation coefficients calculated between the time series of each ROI and those of the entire brain. This process facilitated the generation of z-score plots of FC for individual subjects, illustrating the degree of connectivity relative to the group norm (Smith et al., 2011). To ensure the statistical robustness, the false discovery rate (FDR) approach was operated to correct multiple comparisons. This approach allowed for the precise identification and reporting of significant FC differences between groups, contributing to the understanding of the neurobiological underpinnings of DM1.

Statistical Analysis

The data was carried out based on SPSS software version 24.0 (SPSS Inc., Armonk, NY, USA). Initially, the clinical data were evaluated for normal distribution using the Shapiro-Wilk method to validate the assumptions of statistical tests. The independent sample t-test or Mann-Whitney U test were performed to compare quantitative variables, depending on whether they were following a normal distribution or not. Qualitative variables were compared through the χ² test. We conducted Pearson correlation analysis to explore the relationships between neurocognitive impairments and changes in DC and FC values, along with the covariates of age, sex, and average education years. The statistical significance level was set at p < 0.05 for all statistical analyses. The Bonferroni correction method was also applied.

Demographic and Clinical Characteristics

Participants characteristics were displayed in Table 1. No significant differences were discovered between the DM1 and HC groups in the aspect of age (p = 0.110), sex (p = 0.510), or educational level (p = 0.120). However, significant disparities were found in cognitive assessments; DM1 patients displayed significantly lower scores in the MMSE (p < 0.001), DST-Forward (p < 0.001), DST-Backward (p < 0.001), BNT (p < 0.001), AVLT(p=0.001), and ROCF-copying (p<0.001) compared to HCs. Additionally, DM1 patients tended to require longer times to complete the TMT-A (p = 0.059) and showed significantly longer completion times for the TMT-B (p<0.001).

DC Differences Analysis

DC analysis revealed distinct patterns between the groups. Compared to HCs, DM1 patients exhibited reduced DC values in brain regions, mainly including the bilateral rolandic operculum (ROL), right angular gyrus (ANG.R), left inferior frontal gyrus, triangular part (IFGtriang.L), right median cingulate and paracingulate gyri (DCG.R), and right middle temporal gyrus (MTG.R). In contrast, increased DC values were detected in the right fusiform gyrus (FFG.R), left inferior temporal gyrus (ITG.L), and right hippocampus (HIP.R) among DM1 patients (see Figure 1 and Table 2 for detailed visual and statistical data).

Differences in FC

Figure 2 illustrates a brain connectivity diagram generated by BrainNet Viewer (https://www.nitrc.org/projects/bnv/) software, presenting three views of the brain that depict connections between various regions. Nodes, represented in yellow, denote specific brain areas, while red edges signify increased functional connections. The fMRI analyses of DM1 patients and HCs revealed enhancements in the resting FC in multiple brain regions. Compared to HCs, the current analyses of resting FC in the DM1 group revealed enhancements between MTG.R and ANG.R, as well as between MTG.R and IFGtriang.L. (refer to Table 3).

Correlations Between DC and FC with Clinical Variables

Table 4 presents correlation analyses between DC and FC values with clinical metrics. In the DM1 cohort, the DST Forward and Backward scores exhibited negative correlations with the DC value of the ITG.L (r = -0.539, p = 0.031; r = -0.552, p = 0.027, respectively). Conversely, these scores exhibited positive correlations with the DC value of the HIP.R (r = 0.537, p = 0.032; r = 0.592, p = 0.016, respectively). Additionally, TMT-A completion times were positively correlated with the DC value of the DCG.R (r = 0.666, p = 0.005). The AVLT scores negatively correlated with ITG.L DC values (r = -0.590, p = 0.016), and the ROCF-Copying scores positively correlated with the DC value of the ROL.L (r = 0.523, p = 0.037). Employing the Bonferroni correction, a significant positive correlation was identified between TMT-A scores and DC values of DCG.R (r = 0.666, p = 0.005). Notably, no significant correlations were detected between abnormal FC and neurocognitive performance.

In this investigation, we have for the first time explored the intrinsic dysconnectivity patterns within whole-brain functional networks of DM1 patients through an analysis of DC and secondary FC at the voxel level. Our findings revealed different functional network centralities changed in DM1 patients. Moreover, compared with HCs, DM1 patients exhibited disrupted FC among MTG.R, ANG.R, and IFGtriang.L, which could help to understand the large-scale functional reorganization that associated with cognitive impairments in DM1 patients. Furthermore, DC values in the DCG.R were significantly positively correlated with TMT-A scores, which indicated that the dysfunction of DCG may contribute to poor cognitive performance in DM1 patients.

The presence of cognitive impairments across multiple domains in DM1—including memory, attention, language, executive functions, visuospatial and visuoconstructive abilities, —is well documented and correlates with disease duration and age of onset as highlighted by previous studies (Modoni et al., 2004; Modoni et al., 2008; Sistiaga et al., 2010; Winblad et al., 2006). Our findings corroborated these observations, indicating deterioration across several neuropsychological tests that assess these cognitive domains.

Our analyses demonstrate that DM1 patients exhibit decreased DC values in regions including the ROL, ANG.R, IFGtriang.L, DCG.R, and MTG.R. Conversely, increased DC values were observed in the FFG.R, ITG.L, and HIP.R compared to HCs. This differential pattern of connectivity suggests a reorganization of direct connections within the functional networks of the brain in response to the disease. Furthermore, in our secondary FC analysis using these brain regions as ROIs, we noted enhanced connectivity amongst ANG.R, and IFGtriang.L, providing new insights into the extensive functional reorganization occurring in DM1. Importantly, increased DC in the DCG.R was correlated positively with the completion time for the TMT-A. These findings suggested that abnormalities in DC values detected through brain imaging may serve as potential markers for cognitive impairment in DM1 patients. This study underscores the utility of assessing both DC and secondary FC to advance a better understanding of the underlying mechanisms of neurological dysfunction in DM1, offering avenues for the identification of biomarkers and targets for therapeutic intervention.

DC quantifies the total connectivity of a single voxel with other voxels within the brain (Buckner et al., 2009; van den Heuvel & Sporns, 2013). In our study, patients with DM1 demonstrated reduced DC values in several critical brain regions: ROL, ANG, IFGtriang, DCG, and MTG. The ROL is known to play an important role in integrating exteroceptive and interoceptive signals. Its dysfunction across both hemispheres suggests a marked decline in the self-awareness of DM1 patients, with associated impacts on performance in complex figure copying tasks, as indicated by ROCF test results.

The ANG and MTG are integral parts of the DMN (Buckner et al., 2008), which is primarily responsible for internally directed thought processes including memory, language, and semantic representations, crucial for maintaining human consciousness (Buckner et al., 2008; Menon, 2023). The IFGtriang.L, a component of Broca’s area, is vital for motor speech and semantic control processes (Jackson, 2021; Tremblay & Dick, 2016). Positioned within the frontoparietal control system, the IFGtriang acts as a critical hub linking the sensory/somatomotor network with the DMN and dorsal and ventral attention network, underscoring its multifaceted role in cognitive operation (Luo et al., 2022; Vincent et al., 2008).

The DCG, part of the essential limbic system, is influenced by regulatory neuronal circuits and is primarily associated with inhibitory control (Song et al., 2022). Deficiencies in this area likely affect the patient's ability to manage motor response content, resolve response conflicts, and regulate various cognitive inhibitions (Mirabella, 2021). Notably, our findings link the DC values of the DCG with performance on the TMT-A, highlighting its role in visual-spatial searching and motor response activities (Stuss et al., 2001). Additionally, a regional homogeneity (ReHo)-based neuroimaging meta-analysis (Ma et al., 2022) on mild cognitive impairment (MCI) revealed that acupuncture treatment enhanced activity in the DCG, suggesting that targeted interventions in this region could ameliorate cognitive impairments in these patients.

The observed increases in DC within the FFG, ITG, and HIP in our study suggest a possible adaptive and compensatory mechanism of the brain in DM1 patients. The FFG, situated on the basal surface of the temporal and occipital lobes, plays a pivotal role in sensory integration and cognitive processing. Recent findings have expanded our understanding of the ITG's involvement in decision-making, visual object recognition, impulsivity control and the ventral visual pathway (Yueh-Hsin et al., 2020). Furthermore, the HIP, a crucial component of the limbic system, is extensively linked to essential mental functions including emotion, behavior, learning, and memory (Braun, 2011; Lisman et al., 2017; Opitz, 2014). In our analysis, changes in the DC values of the ITG and HIP were correlated with deficiencies in attention and working memory as measured by the DST (Leung et al., 2011). Additionally, alterations in ITG were associated with performance on the AVLT. These findings align with previous research documenting altered regional activities in the FFG, ITG, and HIP among DM1 patients (Cabada et al., 2021; Hamasaki et al., 2022; Huang et al., 2021; Sugiyama et al., 2017), further corroborating the potential role of these regions in cognitive pathology associated with DM1. It is noteworthy that the data for our study were collected at a single timepoint. Given the progressive nature of DM1 and the potential dynamic changes in brain connectivity, longitudinal studies are essential to fully understand the temporal aspects of these changes. Future research should focus on the trajectory of these brain changes over time, ideally incorporating multiple assessments to capture the evolution of neural adaptations and the efficacy of potential interventions. Such studies would provide deeper insights into the mechanisms of brain plasticity in response to chronic neurological conditions and aid in the development of targeted treatment strategies.

In our ROI-based FC analysis, we observed an increase in FC among the MTG.R, ANG.R, and IFGtriang.L. These regions are predominantly associated with the DMN and the frontoparietal network (FPN), which are integral to higher cognitive functions (Buckner et al., 2008; Vincent et al., 2008). The enhanced connectivity within these networks may elucidate the neural adaptations occurring in response to cognitive demands in DM1 patients. The activation observed in the MTG and ANG may represent compensatory mechanisms within the DMN, potentially facilitating the cognitive process of recollecting past experiences (Raichle, 2015).

The IFGtriang, a pivotal component of the FPN, is known to support decision-making and cognitive control processes (Vincent et al., 2008). The increased connectivity within the DMN and FPN could indicate a neurobiological basis for the observed challenges in DM1 patients to shift cognitive resources from internal thoughts and reflections toward external stimuli effectively. This study suggests that the altered functional connectivity among the MTG, ANG, and IFGtriang may be a critical factor contributing to cognitive decline in DM1 patients. Our findings imply that disruptions in the coordination and communication between whole-brain functional networks responsible for internal mentation and external attentional focus could underpin the cognitive impairments associated with DM1. Future research should further explore these connectivity patterns over time and in response to cognitive interventions, potentially offering insights into the mechanisms of disease progression and avenues for targeted treatment strategies.

Limitations

This study, while providing valuable insights into the functional connectivity patterns in DM1 patients, has several limitations that warrant discussion. Firstly, DM1 is a rare disorder that affects between 0.5 and 18.1 per 100,000 in the population. Consequently, studies involving larger cohorts are necessary to illuminate the diverse mechanisms underlying brain involvement and their association with cognitive impairments observed in DM1. Secondly, our investigation was based solely on resting-state fMRI data. Integrating additional biomarkers, such as genetic profiles or biochemical markers, could enhance the robustness and predictive power of our findings, offering a wider and more comprehensive understanding of the pathophysiological underpinnings of DM1. Thirdly, the fMRI datasets utilized in this study were constrained by limited spatial and temporal resolutions. Enhancements in these technical specifications would likely provide a more detailed and accurate depiction of neural activities and interactions, thereby strengthening the conclusions drawn from our data. Finally, a cross-sectional design limits establishment of causality over time regarding the progression of dynamic changes. Longitudinal studies are imperative to verify the progression patterns identified in this study and to have a better understand of the developmental trajectory of neural alterations in DM1. Such studies could offer crucial insights into the temporal evolution of the disease and the effectiveness of potential interventions, ultimately contributing to improved management strategies for DM1 patients.

Our study has revealed abnormalities in DC and FC within the MTG, ANG, and IFG in patients with DM1. These alterations suggest that these abnormal brain regions could become potential imaging biomarkers for cognitive decline in DM1 patients. The observed changes in connectivity patterns indicate significant neural disruptions, potentially underlying the cognitive deficits frequently observed in this population.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Disclosures:

Authors Qian Sun, Di He, Pei Huang, Zeqi Hao, Jiaxi Zhang, Jun Liu, Xize Jia, Xiaomeng Xue and Haiyan Zhou declare no conflicts of interest.

Funding

This work was supported by grants from the National Natural Science Foundation of China (82371414).

Author Contribution

Qian Sun ,and Di He:Conceptualization, Methodology, Software, Investigation, Formal Analysis, Writing - Original Draft;Pei Huang: Data Curation, Funding Acquisition,Writing - Original Draft;Zeqi Hao: Visualization, Investigation;Jiaxi Zhang:Software, Validation；Jun Liu:Resources, Supervision;Xize Jia: Visualization, Supervision;Xue Xiaomeng and Haiyan Zhou:Conceptualization, Resources, Supervision, Writing - Review & Editing.All authors reviewed the manuscript.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author (Haiyan Zhou) upon reasonable request.

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Table 1 Demographics and Clinical Characteristics of participants

Characteristics	DM1 (n=16)	HCs (n=18)	p value
Age (y)	48.44±14.14	41.50±10.67	0.114
Gender (M/F)	10/6	9/9	0.510
Education (y)	13.00±3.16	14.56±2.53	0.121
Disease duration (y)	6.19±4.82	—	—
MMSE^*	27.06±1.84	29.17±0.92	<0.001*
TMT-A (s)	46.375±23.004	32.894±6.480	0.059
TMT-B (s)^*	120.563±55.226	64.528±15.728	＜0.001*
DST-Forward^*	4.438±1.861	7.722±0.461	＜0.001*
DST-Backward^*	2.563±1.459	6.333±0.594	＜0.001*
BNT^*	23.375±5.439	29.056±0.725	＜0.001*
AVLT^*	57.875±9.507	81.000±4.765	0.001*
ROCF-Copying^*	27.875±6.582	34.444±1.464	＜0.001*

Note: DM1= myotonic dystrophy type 1, HCs= healthy controls, MMSE= mini-mental state examination TMT= trail making test, DST= digit span test, BNT= Boston naming test, AVLT= auditory verbal learning test, ROCF= Rey-Osterrieth complex figure test.

Table 2 Brain regions showing differences in DC in patients with DM1 compared with HCs.

Brain region (AAL)	BA	Cluster size	Peak MNI coordinate			Peak intensity (T value)
Brain region (AAL)	BA	Cluster size	x	y	z	Peak intensity (T value)
Fusiform_R	20	267	36	-12	-39	5.5277
Temporal_Inf_L	20	156	-42	-18	-24	5.8271
Hippocampus_R	36	52	30	-33	-3	5.2157
Angular_R	40	109	45	-57	60	-5.1657
Rolandic_Oper_L	22	89	-60	-3	12	-5.3336
Rolandic_Oper_R	22	60	51	3	3	-6.3329
Frontal_Inf_Tri_L	9	91	-39	21	30	-4.8690
Cingulum_Mid_R	31	85	15	-39	39	-4.9224
Temporal_Mid_R	19	52	51	-69	6	-5.0233

Note: The statistical threshold was set at voxel with P < 0.001 and cluster with P < 0.05 for GRF correction. DC, degree centrality; DM1, myotonic dystrophy type 1; HCs, healthy controls; AAL, Anatomical Automatic Labeling; BA, Brodmann area; MNI, Montreal Neurological Institute; Fusiform_R, right fusiform gyrus; Temporal_Inf_L, left inferior temporal gyrus; Hippocampus_R, right hippocampus; Angular_R, right angular gyrus; Rolandic_Oper_L, left rolandic operculum; Rolandic_Oper_R, right rolandic operculum; Frontal_Inf_Tri_L, left inferior frontal gyrus, triangular part; Cingulum_Mid_R, right median cingulate and paracingulate gyri; Temporal_Mid_R, right middle temporal gyrus.

Table 3 Altered ROI-wise FC between the brain regions showing abnormal DC in patients with DM1 compared with HCs.

Brain region	Brain region	T value	p value
Temporal_Mid_R	Angular_R	3.3528	0.0021
	Frontal_Inf_Tri_L	3.5513	0.0012

Note: The statistical threshold was set at P < 0.05 for FDR correction.

Temporal_Mid_R, right middle temporal gyrus; Angular_R, right angular gyrus; Frontal_Inf_Tri_L, left inferior frontal gyrus, triangular part.

Table 4 Significant correlations between differential brain regions and cognitive tests in DM1 patients

Brain region	Cognition	r	p value
Temporal_Inf_L	DST-Forward	-0.539	0.031
	DST-Backward	-0.552	0.027
	AVLT	-0.590	0.016
Cingulum_Mid_R	TMT-A (s)	0.666	0.005*
Rolandic_Oper_L	ROCF-Copying	0.523	0.037
Hippocampus_R	DST-Forward	0.537	0.032
	DST-Backward	0.592	0.016

Note: DM1= myotonic dystrophy type 1, TMT= trail making test, DST= digit span test, AVLT= auditory verbal learning test, ROCF= Rey-Osterrieth complex figure test, Temporal_Inf_L, left inferior temporal gyrus; Cingulum_Mid_R, right median cingulate and paracingulate gyri; Rolandic_Oper_L, left rolandic operculum; Hippocampus_R, right hippocampus.

No significant correlations were found between abnormal FC and cognitive tests.

No competing interests reported.

Abnormal Degree Centrality and Functional Connectivity Associated with Cognitive Impairment in Myotonic Dystrophy Type 1

Status:

Version 1

Abstract

Figures

Introduction

Subjects and Methods

Results

Discussion

Conclusion

Declarations