Opportunities for Molecular Imaging in Multiple Sclerosis Management: Linking Probe to Treatment

Imaging has been a critical component of multiple sclerosis (MS) management for nearly 40 years. The visual information derived from structural MRI, that is, signs of blood-brain barrier disruption, inflammation and demyelination, and brain and spinal cord atrophy, are the primary metrics used to evaluate therapeutic efficacy in MS. The development of targeted imaging probes has expanded our ability to evaluate and monitor MS and its therapies at the molecular level. Most molecular imaging probes evaluated for MS applications are small molecules initially developed for PET, nearly half of which are derived from U.S. Food and Drug Administration-approved drugs and those currently undergoing clinical trials. Superparamagnetic and fluorinated particles have been used for tracking circulating immune cells (in situ labeling) and immunosuppressive or remyelinating therapeutic stem cells (ex vivo labeling) clinically using proton (hydrogen 1 [1H]) and preclinically using fluorine 19 MRI. Translocator protein PET and 1H MR spectroscopy have been demonstrated to complement imaging metrics from structural (gadolinium-enhanced) MRI in nine and six trials for MS disease-modifying therapies, respectively. Still, despite multiple demonstrations of the utility of molecular imaging probes to evaluate the target location and to elucidate the mechanisms of disease-modifying therapies for MS applications, their use has been sparse in both preclinical and clinical settings

Neuroimaging in Dementia:More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies.</p

Neuroimaging in Dementia: More than Typical Alzheimer Disease

Alzheimer disease (AD) is the most common cause of dementia. The prevailing theory of the underlying pathology assumes amyloid accumulation followed by tau protein aggregation and neurodegeneration. However, the current antiamyloid and antitau treatments show only variable clinical efficacy. Three relevant points are important for the radiologic assessment of dementia. First, besides various dementing disorders (including AD, frontotemporal dementia, and dementia with Lewy bodies), clinical variants and imaging subtypes of AD include both typical and atypical AD. Second, atypical AD has overlapping radiologic and clinical findings with other disorders. Third, the diagnostic process should consider mixed pathologies in neurodegeneration, especially concurrent cerebrovascular disease, which is frequent in older age. Neuronal loss is often present at, or even before, the onset of cognitive decline. Thus, for effective emerging treatments, early diagnosis before the onset of clinical symptoms is essential to slow down or stop subsequent neuronal loss, requiring molecular imaging or plasma biomarkers. Neuroimaging, particularly MRI, provides multiple imaging parameters for neurodegenerative and cerebrovascular disease. With emerging treatments for AD, it is increasingly important to recognize AD variants and other disorders that mimic AD. Describing the individual composition of neurodegenerative and cerebrovascular disease markers while considering overlapping and mixed diseases is necessary to better understand AD and develop efficient individualized therapies

Slowly expanding/evolving lesions as a magnetic resonance imaging marker of chronic active multiple sclerosis lesions.

BACKGROUND:Chronic lesion activity driven by smoldering inflammation is a pathological hallmark of progressive forms of multiple sclerosis (MS). OBJECTIVE:To develop a method for automatic detection of slowly expanding/evolving lesions (SELs) on conventional brain magnetic resonance imaging (MRI) and characterize such SELs in primary progressive MS (PPMS) and relapsing MS (RMS) populations. METHODS:We defined SELs as contiguous regions of existing T2 lesions showing local expansion assessed by the Jacobian determinant of the deformation between reference and follow-up scans. SEL candidates were assigned a heuristic score based on concentricity and constancy of change in T2- and T1-weighted MRIs. SELs were examined in 1334 RMS patients and 555 PPMS patients. RESULTS:Compared with RMS patients, PPMS patients had higher numbers of SELs (p = 0.002) and higher T2 volumes of SELs (p &lt; 0.001). SELs were devoid of gadolinium enhancement. Compared with areas of T2 lesions not classified as SEL, SELs had significantly lower T1 intensity at baseline and larger decrease in T1 intensity over time. CONCLUSION:We suggest that SELs reflect chronic tissue loss in the absence of ongoing acute inflammation. SELs may represent a conventional brain MRI correlate of chronic active MS lesions and a candidate biomarker for smoldering inflammation in MS

Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models

Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort

Early Reduction of MRI Activity During 6 Months of Treatment With Cladribine Tablets for Highly Active Relapsing Multiple Sclerosis

Active Relapsing Multiple Sclerosis; MRI; CladribineEsclerosis Múltiple Recurrente Activa; Imagen por resonancia magnética; CladribinaEsclerosi múltiple recurrent activa; Imatge per ressonància magnètica; CladribinaBackground and Objectives The onset of action for high-efficacy immunotherapies in multiple sclerosis (MS) is an important parameter. This study (MAGNIFY-MS) evaluates the onset of action of cladribine tablets by observing changes in combined unique active (CUA) MRI lesion counts during the first 6 months of treatment in patients with highly active relapsing MS. Methods MRI was performed at screening, baseline, and at months 1, 2, 3, and 6 after initiating treatment with cladribine tablets 3.5 mg/kg. CUA lesion counts, defined as the sum of T1 gadolinium-enhancing (Gd+) lesions and new or enlarging active T2 lesions (without T1 Gd+), were compared between postbaseline and the baseline period and standardized to the period length and the number of MRIs performed. Results Included in this analysis were 270 patients who received ≥1 dose of cladribine tablets. After treatment initiation, significant reductions in mean CUA lesion counts were observed from month 1 onward compared with the baseline period (−1.193 between month 1 and month 6, −1.500 between month 2 and month 6, and −1.692 between month 3 and month 6; all p < 0.0001). Mean T1 Gd+ lesion counts were decreased from month 2 onward compared with baseline (−0.857 at month 2, −1.355 at month 3, and −1.449 at month 6; all p < 0.0001), whereas the proportion of patients without any CUA lesions increased from 52.0% between month 1 and month 6 to 80.5% between month 3 and month 6. Discussion Findings suggest an early onset of action for cladribine tablets, with an increasing reduction in active MRI lesions over time. Trial Registration Information NCT03364036; Date registered: December 06, 2017.This study was supported by the healthcare business of Merck KGaA, Darmstadt, Germany (CrossRef Funder ID: 10.13039/100009945)

DeepBrainPrint: A Novel Contrastive Framework for Brain MRI Re-Identification

Recent advances in MRI have led to the creation of large datasets. With the increase in data volume, it has become difficult to locate previous scans of the same patient within these datasets (a process known as re-identification). To address this issue, we propose an AI-powered medical imaging retrieval framework called DeepBrainPrint, which is designed to retrieve brain MRI scans of the same patient. Our framework is a semi-self-supervised contrastive deep learning approach with three main innovations. First, we use a combination of self-supervised and supervised paradigms to create an effective brain fingerprint from MRI scans that can be used for real-time image retrieval. Second, we use a special weighting function to guide the training and improve model convergence. Third, we introduce new imaging transformations to improve retrieval robustness in the presence of intensity variations (i.e. different scan contrasts), and to account for age and disease progression in patients. We tested DeepBrainPrint on a large dataset of T1-weighted brain MRIs from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and on a synthetic dataset designed to evaluate retrieval performance with different image modalities. Our results show that DeepBrainPrint outperforms previous methods, including simple similarity metrics and more advanced contrastive deep learning frameworks