The sequence of regional structural disconnectivity due to multiple sclerosis lesions

Krakencoder: a unified brain connectome translation and fusion tool

Brain connectivity can be estimated in many ways, depending on modality and processing strategy. Here, we present the Krakencoder, a joint connectome mapping tool that simultaneously bidirectionally translates between structural and functional connectivity, and between different atlases and processing choices via a common latent representation. These mappings demonstrate exceptional accuracy and individual-level identifiability; the mapping between structural and functional connectivity has identifiability 42-54% higher than existing models. The Krakencoder combines all connectome flavors via a shared low-dimensional latent space. This fusion representation better reflects familial relatedness, preserves age- and sex-relevant information, and enhances cognition-relevant information. The Krakencoder can be applied, without retraining, to new out-of-distribution data while still preserving inter-individual differences in the connectome predictions and familial relationships in the latent representations. The Krakencoder is a notable leap forward in capturing the relationship between multimodal brain connectomes in an individualized, behaviorally and demographically relevant way.

Associations between chronic active lesions and clinical outcomes in multiple sclerosis: A systematic literature review

BACKGROUND

Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease. Emerging evidence suggests that chronic disease processes within the central nervous system are important drivers of the ongoing disability accumulation in people with MS (pwMS). Chronic lesion activity driven by smoldering neuroinflammation is considered one of the neuropathological hallmarks of disease progression in worsening disability. Our understanding of the role of chronic active lesions (CALs) in MS pathology has expanded with improvements in imaging technology. Three in vivo imaging biomarkers of CALs are available to detect CALs: paramagnetic rim lesions (PRLs), 18 kDa translocator protein (TSPO)-positron emission tomography rim-positive lesions, and the magnetic resonance imaging (MRI)-defined slowly expanding lesions (SELs).

OBJECTIVE

To evaluate associations between CALs and measures of worsening disability in pwMS.

METHODS

A systematic literature search was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines using PubMed, Embase, and the Cochrane Library on April 21, 2023. The review included randomized controlled trials, retrospective studies, and prospective cross-sectional and longitudinal studies conducted during 2010-2023 reporting the outcomes of interest. Studies evaluating people with any MS phenotype were included if they reported any associative analysis between CALs and clinical outcomes.

RESULTS

A total of 30 of 149 unique studies identified in the literature met the inclusion criteria. Of these 30 publications, 18 were based on PRLs, 9 on MRI-defined SELs, 1 on PRLs and MRI-defined SELs simultaneously, and 2 on TSPO-positive lesions. PRLs were associated with disability worsening in 17 studies, as measured by clinical disability scales. MRI-defined SELs were associated with worsening disability in 10 studies.

CONCLUSIONS

CALs are frequently associated with disease progression and disability accumulation. CALs may provide an indicator of disease severity and may assist with the assessment of treatment efficacy.

Predicting cognition using estimated structural and functional connectivity networks and artificial intelligence in multiple sclerosis

Our prior work demonstrated that estimated structural and functional connectomes (eSC and eFC) generated using multiple sclerosis (MS) lesion masks and artificial intelligence (AI) models can predict disability as effectively as SC and FC derived from diffusion and functional MRI in MS. The goal of this study was to assess the ability of eSC and eFC in predicting baseline and 4-year follow-up cognition in MS patients. The Network Modification tool was performed to estimate SC from the clinical MRI-derived lesion masks. The eSC was then used as an input to Krakencoder, an encoder-decoder model, to estimate FC. The highest accuracy was obtained when predicting the follow-up Symbol Digit Modalities Test (SDMT) using regional eSC or eFC with a median Spearman's correlation of 0.58 for eSC and 0.56 for eFC, which is higher or similar to other studies that predicted cognition in healthy and diseased cohorts. Decreased eSC and eFC in the cerebellum and increased eFC in the default mode network were associated with lower follow-up SDMT scores. Our findings demonstrate that eSC and eFC derived from clinically acquired MRI and AI models can effectively predict cognition. The use of lesion-based estimates of connectome disruptions may potentially improve cognition-related individualized treatment planning.

Conventional MRI-Based Structural Disconnection and Morphometric Similarity Networks and Their Clinical Correlates in Multiple Sclerosis

BACKGROUND AND OBJECTIVES

Although multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses typically require advanced MRI sequences not commonly acquired in clinical practice. Using conventional MRI, we assessed cross-sectional and longitudinal structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.

METHODS

In this longitudinal monocentric study, 3T structural MRI of pwMS and healthy controls (HC) was retrospectively analyzed. Physical and cognitive disabilities were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Demyelinating lesions were automatically segmented, and the corresponding masks were used to assess pairwise structural disconnection between atlas-defined brain regions based on normative tractography data. Using the Morphometric Inverse Divergence method, we computed morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its extension NBS-predict, we tested whether subject-level connectomes were associated with disease status, progression, clinical disability, and long-term confirmed disability progression (CDP), independently from global lesion burden and atrophy.

RESULTS

We studied 461 pwMS (age = 37.2 ± 10.6 years, F/M = 324/137), corresponding to 1,235 visits (mean follow-up time = 1.9 ± 2.0 years, range = 0.1-13.3 years), and 55 HC (age = 42.4 ± 15.7 years; F/M = 25/30). Long-term clinical follow-up was available for 285 pwMS (mean follow-up time = 12.4 ± 2.8 years), 127 of whom (44.6%) exhibited CDP. At baseline, structural disconnection in pwMS was mostly centered around the thalami and cortical sensory and association hubs, while morphometric similarity was extensively disrupted (pFWE < 0.01). EDSS was related to frontothalamic disconnection (pFWE < 0.01) and disrupted morphometric similarity around the left perisylvian cortex (pFWE = 0.02), while SDMT was associated with cortico-subcortical disconnection in the left hemisphere (pFWE < 0.01). Longitudinally, both structural disconnection and morphometric similarity disruption significantly progressed (pFWE = 0.04 and pFWE < 0.01), correlating with EDSS increase (ρ = 0.07, p = 0.02 and ρ = 0.11, p < 0.001), while baseline disconnection predicted long-term CDP (accuracy = 59% [58-60], p = 0.03).

DISCUSSION

Structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its progression. They explain disease-related clinical disability and predict its long-term evolution independently from global lesion burden and atrophy, potentially adding to established MRI measures as network-based biomarkers of disease severity and progression.

Sequence of episodic memory-related behavioral and brain-imaging abnormalities in type 2 diabetes

BACKGROUND

Episodic memory decline is a common complication of type 2 diabetes (T2D). To comprehensively explore the neural mechanisms underlying it, we aimed to explore the sequence that episodic memory-related behavioral and brain-imaging biomarkers appear abnormal in the progression of T2D.

METHODS

We enrolled 62 healthy controls and 110 patients with T2D. The California Verbal Learning Test, Montreal cognitive assessment, and Stroop color word test was used to assess the episodic memory, general cognitive function, and executive function. Principal component analysis was applied to extract behavioral biomarkers. Imaging biomarkers included structural and functional MRI features of the entorhinal cortex-hippocampus and hippocampus-anterior cingulate cortex pathways. We used a novel discriminative event-based model to determine the sequence that memory-related biomarkers appear abnormal and estimate the stage of memory decline.

RESULTS

T2D patients exhibited poorer memory, general cognitive function, and executive function compared to healthy controls after controlling age, sex, and education level. In the progression of T2D, functional interaction between brain regions showed abnormalities first, followed by memory tests, the cerebral spontaneous neural activity, and finally the gray matter volume. Besides, abnormalities appeared earlier in the entorhinal cortex than in the anterior cingulate cortex. Later stage of memory decline was distributed in older patients with T2D and was associated with higher systolic blood pressure, postprandial blood glucose, and low-density lipoprotein.

CONCLUSIONS

In T2D, behavioral and brain imaging biomarkers of episodic memory appear abnormal in a specific sequence, and the stage of memory decline was closely associated with old age and vascular risk factors.

CLINICAL TRIAL REGISTRATION

NCT02420470, ClinicalTrials.gov ( https://www.

CLINICALTRIALS

gov/ ).

TSPO-PET Reveals Higher Inflammation in White Matter Disrupted by Paramagnetic Rim Lesions in Multiple Sclerosis

Objective

To explore whether the inflammatory activity is higher in white matter (WM) tracts disrupted by paramagnetic rim lesions (PRLs) and if inflammation in PRL-disrupted WM tracts is associated with disability in people with multiple sclerosis (MS).

Methods

Forty-four MS patients and 16 healthy controls were included. 18 kDa-translocator protein positron emission tomography (TSPO-PET) with the 11C-PK11195 radioligand was used to measure the neuroinflammatory activity. The Network Modification Tool was used to identify WM tracts disrupted by PRLs and non-PRLs that were delineated on MRI. The Expanded Disability Status Scale was used to measure disability.

Results

MS patients had higher inflammatory activity in whole brain WM compared to healthy controls (p=0.001). Compared to patients without PRLs, patients with PRLs exhibited higher levels of inflammatory activity in the WM tracts disrupted by any type of lesions (p=0.02) or PRLs (p=0.004). In patients with at least one PRL, inflammatory activity was higher in WM tracts highly disrupted by PRLs compared to WM tracts highly disrupted by non-PRLs (p=0.009). Elevated inflammatory activity in highly disrupted WM tracts was associated with increased disability in patients with PRL (p=0.03), but not in patients without PRL (p=0.2).

Interpretation

This study suggests that patients with PRLs may exhibit more diffuse WM inflammation in addition to higher inflammation along WM tracts disrupted by PRLs compared to non-PRLs, which could contribute to larger lesion volumes and faster disability progression. Imaging PRLs may serve to identify patients with both focal and diffuse inflammation, guiding therapeutic interventions aimed at reducing inflammation and preventing progressive disability in MS.

Cortical lesions impact cognitive decline in multiple sclerosis via volume loss of nonlesional cortex

OBJECTIVE

To assess the interrelationship between cortical lesions and cortical thinning and volume loss in people with multiple sclerosis within cortical networks, and how this relates to future cognition.

METHODS

In this longitudinal study, 230 people with multiple sclerosis and 60 healthy controls underwent 3 Tesla MRI at baseline and neuropsychological assessment at baseline and 5-year follow-up. Cortical regions (N = 212) were divided into seven functional networks. Regions were defined as either lesional or normal-appearing cortex based on presence of a cortical lesion on artificial intelligence-generated double inversion-recovery scans. Cortical volume and thickness were determined within lesional or normal-appearing cortex.

RESULTS

Prevalence of at least one cortical lesion was highest in the limbic (73%) followed by the default mode network (70.9%). Multiple sclerosis-related cortical thinning was more pronounced in lesional (mean Z-score = 0.70 ± 0.84) compared to normal-appearing cortex (-0.45 ± 0.60; P < 0.001) in all, except sensorimotor, networks. Cognitive dysfunction, particularly of verbal memory, visuospatial memory, and inhibition, at follow-up was best predicted by baseline network volume of normal-appearing cortex of the default mode network [B (95% CI) = 0.31 (0.18; 0.43), P < 0.001]. Mediation analysis showed that the effect of cortical lesions on future cognition was mediated by volume loss of the normal-appearing instead of lesional cortex, independent of white matter lesion volume.

INTERPRETATION

Multiple sclerosis-related cortical thinning was worse in lesional compared to normal-appearing cortex, while volume loss of normal-appearing cortex was most predictive of subsequent cognitive decline, particularly in the default mode network. Mediation analyses indicate that cortical lesions impact cognitive decline plausibly by inducing atrophy, rather than through a direct effect.

Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review

Multiple sclerosis (MS) is a demyelinating neurological disorder with a highly heterogeneous clinical presentation and course of progression. Disease-modifying therapies are the only available treatment, as there is no known cure for the disease. Careful selection of suitable therapies is necessary, as they can be accompanied by serious risks and adverse effects such as infection. Magnetic resonance imaging (MRI) plays a central role in the diagnosis and management of MS, though MRI lesions have displayed only moderate associations with MS clinical outcomes, known as the clinico-radiological paradox. With the advent of machine learning (ML) in healthcare, the predictive power of MRI can be improved by leveraging both traditional and advanced ML algorithms capable of analyzing increasingly complex patterns within neuroimaging data. The purpose of this review was to examine the application of MRI-based ML for prediction of MS disease progression. Studies were divided into five main categories: predicting the conversion of clinically isolated syndrome to MS, cognitive outcome, EDSS-related disability, motor disability and disease activity. The performance of ML models is discussed along with highlighting the influential MRI-derived biomarkers. Overall, MRI-based ML presents a promising avenue for MS prognosis. However, integration of imaging biomarkers with other multimodal patient data shows great potential for advancing personalized healthcare approaches in MS.

Reduced oxygen extraction fraction in deep cerebral veins associated with cognitive impairment in multiple sclerosis

Studying the relationship between cerebral oxygen utilization and cognitive impairment is essential to understanding neuronal functional changes in the disease progression of multiple sclerosis (MS). This study explores the potential of using venous susceptibility in internal cerebral veins (ICVs) as an imaging biomarker for cognitive impairment in relapsing-remitting MS (RRMS) patients. Quantitative susceptibility mapping derived from fully flow-compensated MRI phase data was employed to directly measure venous blood oxygen saturation levels (SvO2) in the ICVs. Results revealed a significant reduction in the susceptibility of ICVs (212.4 ± 30.8 ppb vs 239.4 ± 25.9 ppb) and a significant increase of SvO2 (74.5 ± 1.89% vs 72.4 ± 2.23%) in patients with RRMS compared with age- and sex-matched healthy controls. Both the susceptibility of ICVs (r = 0.508, p = 0.031) and the SvO2 (r = -0.498, p = 0.036) exhibited a moderate correlation with cognitive decline in these patients assessed by the Paced Auditory Serial Addition Test, while no significant correlation was observed with clinical disability measured by the Expanded Disability Status Scale. The findings suggest that venous susceptibility in ICVs has the potential to serve as a specific indicator of oxygen metabolism and cognitive function in RRMS. .

Network Analysis in Multiple Sclerosis and Related Disorders

Multiple sclerosis (MS) is a neuroinflammatory and neurodegenerative disease of the central nervous system, commonly featuring disability and cognitive impairment. The pathologic hallmark of MS lies in demyelination and hence impaired structural and functional neuronal pathways. Recent studies have shown that MS shows extensive structural disconnection of key network hub areas like the thalamus, combined with a functional network reorganization that can mostly be related to poorer clinical functioning. As MS can, therefore, be considered a network disorder, this review outlines recent innovations in the field of network neuroscience in MS.

Reduced Oxygen Extraction Fraction in Deep Cerebral Veins Associated with Cognitive Impairment in Multiple Sclerosis

Studying the relationship between cerebral oxygen utilization and cognitive impairment is essential to understanding neuronal functional changes in the disease progression of multiple sclerosis (MS). This study explores the potential of using venous susceptibility in internal cerebral veins (ICVs) as an imaging biomarker for cognitive impairment in relapsing-remitting MS (RRMS) patients. Quantitative susceptibility mapping derived from fully flow-compensated MRI phase data was employed to directly measure venous blood oxygen saturation levels (SvO2) in the ICVs. Results revealed a significant reduction in the susceptibility of ICVs (212.4 ± 30.8 ppb vs 239.4 ± 25.9 ppb) and a significant increase of SvO2 (74.5 ± 1.89 % vs 72.4 ± 2.23 %) in patients with RRMS compared with age- and sex-matched healthy controls. Both the susceptibility of ICVs (r = 0.646, p = 0.004) and the SvO2 (r = -0.603, p = 0.008) exhibited a strong correlation with cognitive decline in these patients assessed by the Paced Auditory Serial Addition Test, while no significant correlation was observed with clinical disability measured by the Expanded Disability Status Scale. The findings suggest that venous susceptibility in ICVs has the potential to serve as a specific indicator of oxygen metabolism and cognitive function in RRMS.