Estimated connectivity networks outperform observed connectivity networks when classifying people with multiple sclerosis into disability groups

Background: Multiple Sclerosis (MS), a neurodegenerative and neuroinflammatory disease, causing lesions that disrupt the brain's anatomical and physiological connectivity networks, resulting in cognitive, visual and/or motor disabilities. Advanced imaging techniques like diffusion and functional MRI allow measurement of the brain's structural connectivity (SC) and functional connectivity (FC) networks, and can enable a better understanding of how their disruptions cause disability in people with MS (pwMS). However, advanced MRI techniques are used mainly for research purposes as they are expensive, time-consuming, and require high-level expertise to acquire and process. As an alternative, the Network Modification (NeMo) Tool can be used to estimate SC and FC using lesion masks derived from pwMS and a reference set of controls' connectivity networks. Objective: Here, we test the hypothesis that estimated SC and FC (eSC and eFC)from the NeMo Tool, based only on an individual's lesion masks, can be used to classify pwMS into disability categories just as well as SC and FC extracted from advanced MRI directly in pwMS. We also aim to find the connections most important for differentiating between no disability vs evidence of disability groups. Materials and Methods: One hundred pwMS (age:45.51 ± 1.4 years, 66% female, disease duration: 12.97 ± 8.07 years) were included in this study. Expanded DisabilityStatus Scale (EDSS) was used to assess disability, 67 pwMS had no disability (EDSS<2). Observed SC and FC were extracted from diffusion and functional MRI directly in pwMS, respectively. The NeMo Tool was used to estimate the remaining structural connectome (eSC), by removing streamlines in a reference set of tractograms that intersected the lesion mask. The NeMo Tool's eSC was used then as input to a deep neural network to estimate the corresponding FC (eFC). Logistic regression with ridge regularization was used to classify pwMS into disability categories (no disability vs evidence of disability), based on demographics/clinical information (sex, age, race, disease duration, clinical phenotype, and spinal lesion burden) and either pairwise entries or regional summaries from one of the following matrices: SC, FC, eSC, and eFC. The area under the ROC curve (AUC) was used to assess the classification performance. Both univariate statistics and parameter coefficients from the classification models were used to identify features important to differentiating between the groups. Results: The regional eSC and eFC models outperformed their observed FC andSC counterparts (p-value<0.05), while the pairwise eSC and SC performed similarly (p=0.10). Regional eSC and eFC models had higher AUC (0.66-0.68) than the pair-wise models (0.60-0.65), with regional eFC having highest classification accuracy across all models. Ridge regression coefficients for the regional eFC and regional observed FC models were significantly correlated (Pearson's r= 0.52, p-value<10e-7). Decreased estimated SC node strength in default mode and ventral attention networks and increased eFC node strength in visual networks were associated with evidence of disability. Discussion: Here, for the first time, we use clinically-acquired lesion masks to estimate both structural and functional connectomes in patient populations to better understand brain lesion-dysfunction mapping in pwMS. Models based on the NeMo Tool's estimates of SC and FC better classified pwMS by disability level than SC and FC observed directly in the individual using advanced MRI. This work provides a viable alternative to performing high-cost, advanced MRI in patient populations, bringing the connectome one step closer to the clinic.

Download Full-text

Structural and functional connectomes in people with multiple sclerosis

10.1101/2020.08.25.20181727 ◽

2020 ◽

Author(s):

Ceren Tozlu ◽

Keith Jamison ◽

Susan Gauthier ◽

Amy Kuceyeski

Keyword(s):

Multiple Sclerosis ◽

Functional Mri ◽

Imaging Modality ◽

Structural Connectivity ◽

Receiver Operating Curve ◽

Ensemble Prediction ◽

P Value ◽

Lesion Volume ◽

Healthy Controls ◽

Functional Connectome

One of the challenges in multiple sclerosis is that lesion volume does not correlate with symptom severity. Advanced techniques such as diffusion and functional MRI allow imaging of the brain's connectivity networks, which may provide better insight as to brain-behavior relationships in impairment and compensation in multiple sclerosis. We aim to build machine learning models based on structural and functional connectomes to classify a) healthy controls versus people with multiple sclerosis and b) impaired versus not impaired people with multiple sclerosis. We also aim to identify the most important imaging modality for both classification tasks, and, finally, to investigate which brain regions' connectome measures contribute most to the classification. Fifteen healthy controls (age=43.6 ± 8.6, 53% female) and 76 people with multiple sclerosis (age: 45.2 ± 11.4 years, 65% female, disease duration: 12.2 ± 7.2 years) were included. Twenty-three people with multiple sclerosis were considered impaired, with an Expanded Disability Status Scale of 2 or higher. Subjects underwent MRI scans that included anatomical, diffusion and resting-state functional MRI. Random Forest models were constructed using structural and static/dynamic functional connectome measures independently; single modality models were then combined for an ensemble prediction. The accuracy of the models was assessed by the area under the receiver operating curve. Models that included structural connectomes significantly outperformed others when classifying healthy controls and people with multiple sclerosis, having a median accuracy of 0.86 (p-value<0.05, corrected). Models that included dynamic functional connectome metrics significantly outperformed others when distinguishing people with multiple sclerosis by impairment level, having a median accuracy of 0.63 (p-value<0.05, corrected). Structural connectivity between subcortical, somatomotor, and visual networks was most damaged by multiple sclerosis. For the classification of patients with multiple sclerosis into impairment severity groups, the most discriminatory metric was dwell time in a dynamic functional connectome state characterized by strong connectivity between and among somatomotor and visual networks. These results suggest that damage to the structural connectome, particularly in the subcortical, visual, and somatomotor networks, is a hallmark of multiple sclerosis, and, furthermore, that increased functional coordination between these same regions may be related to severity of motor disability in multiple sclerosis. The use of multi-modal connectome imaging has the potential to shed light on mechanisms of disease and compensation in multiple sclerosis, thus enabling more accurate prognoses and possibly the development of novel therapeutics.

Download Full-text

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis

Frontiers in Neuroscience ◽

10.3389/fnins.2021.763966 ◽

2021 ◽

Vol 15 ◽

Author(s):

Ceren Tozlu ◽

Keith Jamison ◽

Susan A. Gauthier ◽

Amy Kuceyeski

Keyword(s):

Multiple Sclerosis ◽

Functional Connectivity ◽

Functional Mri ◽

Imaging Techniques ◽

Clinical Information ◽

Brain Regions ◽

Receiver Operating Curve ◽

Compensatory Mechanisms ◽

Disability Status ◽

Secondary Objective

Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status.Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks.Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC).Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability.Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.

Download Full-text

The Possible Direct Correlation between Cognitive Impairment and Fear of Catching COVID-19 among Patients with Multiple Sclerosis

Iranian Journal of Psychiatry ◽

10.18502/ijps.v16i3.6260 ◽

2021 ◽

Author(s):

Roghayyeh Saeedi ◽

Nasim Rezaeimanesh ◽

Mohammad Ali Sahraian ◽

Abdorreza Naser Moghadasi

Keyword(s):

Multiple Sclerosis ◽

Cognitive Impairment ◽

Cognitive Disorders ◽

Clinical Information ◽

Immunosuppressive Drugs ◽

Cognitive Disorder ◽

P Value ◽

Information Processing Speed ◽

Cross Sectional ◽

The Relationship

Objective: The prevalence of cognitive impairment in multiple sclerosis (MS) is significant and it is estimated that 40% to 70% of patients with MS suffer from this impairment. COVID-19 is also a new infectious disease. The symptoms of this disease, which include fever, shortness of breath, and cough, can be mild to severe and can even lead to death. Due to the use of immunosuppressive drugs by Patients with MS, they might be at greater risk of catching COVID-19. Thus, patients with MS may be more afraid of catching the virus. One of the important factors is the relationship between cognitive deficit and the increase in patients' fear of COVID-19. The aim of this study was to assess the relationship between fear of catching COVID-19 and cognitive impairment in patients with MS. Method: This cross-sectional study was conducted at the MS Clinic, Sina hospital, Tehran University of Medical Sciences, Tehran, Iran. Our participants in this project were Patients with MS who were over 18 years old and had no history of other neurological and psychiatric diseases. In addition to obtaining demographic and clinical information, we measured the fear of catching the COVID 2019 via Fear of COVID-19 Scale (FCV-19S), which is 7-item questionnaire. We also used Multiple Sclerosis Neuro Psychological Screening Questionnaire (MSNQ) to assess memory and information processing speed in Patients with MS. Results: After adjustment for age, gender, disease duration, highest level of education, MS type, and EDSS in linear regression model, as well as the MSNQ total score and fear score of catching coronavirus, the results demonstrated a significant positive correlation with P value of 0.00 and β: 0.024. Conclusion: The present study showed a direct relationship between cognitive disorder and level of fear regarding COVID-19. Patients with more cognitive disorders were more afraid of COVID-19.

Download Full-text

Brief Overview and Experience of Visual Evoked Potential of First 67 cases at Referral Neuroscience Hospital in Bangladesh

Journal of National Institute of Neurosciences Bangladesh ◽

10.3329/jninb.v6i2.50744 ◽

2020 ◽

Vol 6 (2) ◽

pp. 74-77

Author(s):

Mohammad Enayet Hussain ◽

Bithi Debnath ◽

AFM Al Masum Khan ◽

Md Ferdous Mian ◽

Md Nahidul Islam ◽

...

Keyword(s):

Multiple Sclerosis ◽

Optic Neuritis ◽

Clinical Diagnosis ◽

Optic Neuropathy ◽

Cross Sectional Study ◽

P Value ◽

Pattern Reversal ◽

Cross Sectional ◽

The Mean ◽

Visual Evoked

Background: The visual evoked potentials (VEP) is a valuable tool to document occult lesions of the central visual channels especially within the optic nerve. Objectives: The purpose of the present study was to observe the findings of first few cases of VEP done in the neurophysiology department of the National Institute of Neurosciences (NINS), Dhaka, Bangladesh. Methodology: This cross-sectional study was conducted in the Department of Neurophysiology at the National Institute of Neurosciences and Hospital, Dhaka, Bangladesh from September 2017 to March 2020. All patients referred to the Neurophysiology Department of NINS for VEP were included. Pattern reversal VEPs were done using standard protocol set by International Federation of Clinical Neurophysiology (IFCN). Results: The mean age of the study population was 30.70 (±12.11) years (6-68 years) with 31 (46.3%) male and 36 (53.7%) female patients. The mean duration of illness was 8.71 (±1.78) months (3 days- 120 months). Most common presenting symptom was blurring of vision (37.3%) and dimness of vision (32.8%). Patterned VEP revealed mixed type (both demyelinating and axonal) of abnormality in most cases [29(43.35)]. The most common clinical diagnosis was multiple sclerosis (29.85%) and optic neuropathy (26.87%). In the clinically suspected cases of multiple sclerosis, optic neuropathy and optic neuritis most of the cases of VEP were abnormal and the p value is 0.04 in optic neuropathy and optic neuritis. Conclusion: The commonest presentation of the patients in this series were blurring of vision and dimness of vision. The most common clinical diagnosis for which VEP was asked for, was optic neuritis and multiple sclerosis. Most abnormalities were of mixed pattern (demyelinating and axonal). Journal of National Institute of Neurosciences Bangladesh, 2020;6(2): 74-77

Download Full-text

Faculty Opinions recommendation of Clinical correlations of brain lesion distribution in multiple sclerosis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1158633.618843 ◽

2009 ◽

Author(s):

Nancy Richert ◽

Francesca Bagnato

Keyword(s):

Multiple Sclerosis ◽

Brain Lesion ◽

Clinical Correlations

Download Full-text

CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes

Scientific Reports ◽

10.1038/s41598-021-83591-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maria L. Elkjaer ◽

Arkadiusz Nawrocki ◽

Tim Kacprowski ◽

Pernille Lassen ◽

Anja Hviid Simonsen ◽

...

Keyword(s):

Multiple Sclerosis ◽

Protein Phosphatase ◽

Brain Lesion ◽

Brain Lesions ◽

Cns Diseases ◽

Secondary Progressive ◽

Apolipoprotein A ◽

Proteomic Approach ◽

Apolipoprotein C ◽

Altered Proteins

AbstractTo identify markers in the CSF of multiple sclerosis (MS) subtypes, we used a two-step proteomic approach: (i) Discovery proteomics compared 169 pooled CSF from MS subtypes and inflammatory/degenerative CNS diseases (NMO spectrum and Alzheimer disease) and healthy controls. (ii) Next, 299 proteins selected by comprehensive statistics were quantified in 170 individual CSF samples. (iii) Genes of the identified proteins were also screened among transcripts in 73 MS brain lesions compared to 25 control brains. F-test based feature selection resulted in 8 proteins differentiating the MS subtypes, and secondary progressive (SP)MS was the most different also from controls. Genes of 7 out these 8 proteins were present in MS brain lesions: GOLM was significantly differentially expressed in active, chronic active, inactive and remyelinating lesions, FRZB in active and chronic active lesions, and SELENBP1 in inactive lesions. Volcano maps of normalized proteins in the different disease groups also indicated the highest amount of altered proteins in SPMS. Apolipoprotein C-I, apolipoprotein A-II, augurin, receptor-type tyrosine-protein phosphatase gamma, and trypsin-1 were upregulated in the CSF of MS subtypes compared to controls. This CSF profile and associated brain lesion spectrum highlight non-inflammatory mechanisms in differentiating CNS diseases and MS subtypes and the uniqueness of SPMS.

Download Full-text

Serum neurofilament light chain as a severity marker for spinocerebellar ataxia

Scientific Reports ◽

10.1038/s41598-021-92855-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Hye-Rim Shin ◽

Jangsup Moon ◽

Woo-Jin Lee ◽

Han Sang Lee ◽

Eun Young Kim ◽

...

Keyword(s):

Spinocerebellar Ataxia ◽

Disease Duration ◽

Trinucleotide Repeat ◽

Neuronal Damage ◽

Demographic Data ◽

Brain Magnetic Resonance Imaging ◽

Clinical Severity ◽

P Value ◽

Repeat Number ◽

Neurofilament Light

AbstractSince the serum neurofilament light (NfL) chain is known as a promising biomarker in neurodegenerative diseases, we aimed to evaluate serum NfL as a biomarker indicating neuronal damage in autosomal-dominant (AD) spinocerebellar ataxia (SCA). We reviewed patients diagnosed with AD SCA in the outpatient clinic of Seoul National University Hospital’s (SNUH) Department of Neurology between May and August of 2019. We reviewed the demographic data, clinical characteristics, Scale for the Assessment and Rating of Ataxia (SARA) score, and brain magnetic resonance imaging (MRI) scans. The serum NfL was measured by electrochemiluminescence (ECL) immunoassay. Forty-nine patients with AD SCA were reviewed and their serum NfL level was determined. The median serum NfL level (109.5 pg/mL) was higher than control (41.1 pg/mL) (p-value < 0.001). Among the AD SCA patients, there was a positive correlation between the serum NfL level and the trinucleotide repeat number (r = 0.47, p-value = 0.001), disease duration (r = 0.35, p-value = 0.019), disease duration/age × trinucleotide repeat number (r = 0.330, p-value = 0.021), and SARA score (n = 33; r = 0.37, p-value = 0.033). This study shows that serum NfL is elevated in AD SCA patients and correlates with clinical severity.

Download Full-text

The Role of White Matter Disconnection in the Symptoms Relating to the Anarchic Hand Syndrome: A Single Case Study

Brain Sciences ◽

10.3390/brainsci11050632 ◽

2021 ◽

Vol 11 (5) ◽

pp. 632

Author(s):

Valentina Pacella ◽

Giuseppe Kenneth Ricciardi ◽

Silvia Bonadiman ◽

Elisabetta Verzini ◽

Federica Faraoni ◽

...

Keyword(s):

White Matter ◽

Single Case ◽

Brain Lesion ◽

Structural Connectivity ◽

Disconnection Syndrome ◽

Neuroimaging Data ◽

Neuropsychological Investigation ◽

Anterior Posterior

The anarchic hand syndrome refers to an inability to control the movements of one’s own hand, which acts as if it has a will of its own. The symptoms may differ depending on whether the brain lesion is anterior, posterior, callosal or subcortical, but the relative classifications are not conclusive. This study investigates the role of white matter disconnections in a patient whose symptoms are inconsistent with the mapping of the lesion site. A repeated neuropsychological investigation was associated with a review of the literature on the topic to identify the frequency of various different symptoms relating to this syndrome. Furthermore, an analysis of the neuroimaging regarding structural connectivity allowed us to investigate the grey matter lesions and white matter disconnections. The results indicated that some of the patient’s symptoms were associated with structures that, although not directly damaged, were dysfunctional due to a disconnection in their networks. This suggests that the anarchic hand may be considered as a disconnection syndrome involving the integration of multiple antero-posterior, insular and interhemispheric networks. In order to comprehend this rare syndrome better, the clinical and neuroimaging data need to be integrated with the clinical reports available in the literature on this topic.

Download Full-text

POS1465-HPR THE RELATIONSHIP BETWEEN SEDENTARY BEHAVIOR AND SLEEP IN RHEUMATOID ARTHRITIS: A CROSS-SECTIONAL STUDY

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.641 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1017.2-1018

Author(s):

N. Kelly ◽

E. Hawkins ◽

H. O’leary ◽

K. Quinn ◽

G. Murphy ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Physical Activity ◽

Sleep Quality ◽

Sedentary Behavior ◽

Disease Duration ◽

Cross Sectional Study ◽

P Value ◽

Poor Sleep ◽

Cross Sectional ◽

The Relationship

Background:Rheumatoid arthritis (RA) is a chronic, autoimmune inflammatory condition that affects 0.5% of the adult population worldwide (1). Sedentary behavior (SB) is any waking behavior characterized by an energy expenditure of ≤1.5 METs (metabolic equivalent) and a sitting or reclining posture, e.g. computer use (2) and has a negative impact on health in the RA population (3). Sleep is an important health behavior, but sleep quality is an issue for people living with RA (4, 5). Poor sleep quality is associated with low levels of physical activity in RA (4) however the association between SB and sleep in people who have RA has not been examined previously.Objectives:The aim of this study was to investigate the relationship between SB and sleep in people who have RA.Methods:A cross-sectional study was conducted. Patients were recruited from rheumatology clinics in a large acute public hospital serving a mix of urban and rural populations. Inclusion criteria were diagnosis of RA by a rheumatologist according to the American College of Rheumatology criteria age ≥ 18 and ≤ 80 years; ability to mobilize independently or aided by a stick; and to understand written and spoken English. Demographic data on age, gender, disease duration and medication were recorded. Pain and fatigue were measured by the Visual Analogue Scale (VAS), anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS), and sleep quality was assessed using the Pittsburgh Sleep Quality Index. SB was measured using the ActivPAL4™ activity monitor, over a 7-day wear period. Descriptive statistics were calculated to describe participant characteristics. Relationships between clinical characteristics and SB were examined using Pearson’s correlation coefficients and regression analyses.Results:N=76 participants enrolled in the study with valid data provided by N=72 participants. Mean age of participants was 61.5years (SD10.6) and the majority 63% (n = 47) were female. Participant mean disease duration was 17.8years (SD10.9). Mean SB time was 533.7 (SD100.1) minutes (8.9 hours per day/59.9% of waking hours). Mean sleep quality score was 7.2 (SD5.0) (Table 1). Correlation analysis and regression analysis found no significant correlation between sleep quality and SB variables. Regression analysis demonstrated positive statistical associations for SB time and body mass index (p-value=0.03846, R2 = 0.05143), SB time and pain VAS (p-value=0.009261, R2 = 0.07987), SB time and HADS (p-value = 0.009721, R2 = 0.08097) and SB time and HADSD (p-value = 0.01932, R2 = 0.0643).Conclusion:We found high levels of sedentary behavior and poor sleep quality in people who have RA, however no statistically significant relationship was found in this study. Future research should further explore the complex associations between sedentary behavior and sleep quality in people who have RA.References:[1]Carmona L, et al. Rheumatoid arthritis. Best Pract Res Clin Rheumatol 2010;24:733–745.[2]Anon. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours”. Appl Physiol Nutr Metab = Physiol Appl Nutr Metab 2012;37:540–542.[3]Fenton, S.A.M. et al. Sedentary behaviour is associated with increased long-term cardiovascular risk in patients with rheumatoid arthritis independently of moderate-to-vigorous physical activity. BMC Musculoskelet Disord 18, 131 (2017).[4]McKenna S, et al. Sleep and physical activity: a cross-sectional objective profile of people with rheumatoid arthritis. Rheumatol Int. 2018 May;38(5):845-853.[5]Grabovac, I., et al. 2018. Sleep quality in patients with rheumatoid arthritis and associations with pain, disability, disease duration, and activity. Journal of clinical medicine, 7(10)336.Table 1.Sleep quality in people who have RASleep variableBed Time N(%) before 10pm13(18%) 10pm-12pm43 (60%) after 12pm16 (22%)Hours Sleep mean(SD)6.56 (1.54)Fall Asleep minutes mean(SD)33.3(27.7)Night Waking N(%)45(63%)Self-Rate Sleep mean(SD)2.74 (0.90)Hours Sleep mean(SD)6.56 (1.54)Disclosure of Interests:None declared

Download Full-text