Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes

Gray Matter Morphometry Correlates with Attentional Efficiency in Young-Adult Multiple Sclerosis

Slowed processing on the alerting, orienting and executive control components of attention measured using the Attention Network Test-Interactions (ANT-I) have been widely reported in multiple sclerosis (MS). Despite the assumption that these components correspond to specific neuroanatomical networks in the brain, little is known about gray matter changes that occur in MS and their association with ANT-I performance. We investigated vertex-wise cortical thickness changes and deep gray matter volumetric changes in young MS participants (N = 21, age range: 18–35) with pediatric or young-adult onset and mild disease severity. ANT-I scores and cortical thickness were not significantly different between MS participants and healthy volunteers (N = 19, age range: 18–35), but thalamic volumes were significantly lower in MS. Slowed reaction times on the alerting component in MS correlated significantly with reduced volume of the right pallidum in MS. Slowed reaction times on executive control component correlated significantly with reduced thickness in the frontal, parietal and visual cortical areas and with reduced volume of the left putamen in MS. These findings demonstrate associations between gray matter changes and attentional performance even in the absence of widespread atrophy or slowed attentional processes

Gray Matter Morphometry Correlates with Attentional Efficiency in Young-Adult Multiple Sclerosis

Slowed processing on the alerting, orienting and executive control components of attention measured using the Attention Network Test-Interactions (ANT-I) have been widely reported in multiple sclerosis (MS). Despite the assumption that these components correspond to specific neuroanatomical networks in the brain, little is known about gray matter changes that occur in MS and their association with ANT-I performance. We investigated vertex-wise cortical thickness changes and deep gray matter volumetric changes in young MS participants (N = 21, age range: 18–35) with pediatric or young-adult onset and mild disease severity. ANT-I scores and cortical thickness were not significantly different between MS participants and healthy volunteers (N = 19, age range: 18–35), but thalamic volumes were significantly lower in MS. Slowed reaction times on the alerting component in MS correlated significantly with reduced volume of the right pallidum in MS. Slowed reaction times on executive control component correlated significantly with reduced thickness in the frontal, parietal and visual cortical areas and with reduced volume of the left putamen in MS. These findings demonstrate associations between gray matter changes and attentional performance even in the absence of widespread atrophy or slowed attentional processes

Beyond focal cortical lesions in MS: an in vivo quantitative and spatial imaging study at 7T

Objectives: Using quantitative T2* 7-tesla (7T) MRI as a marker of demyelination and iron loss, we investigated, in patients with relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS), spatial and tissue intrinsic characteristics of cortical lesion(s) (CL) types, and structural integrity of perilesional normal-appearing cortical gray matter (NACGM) as a function of distance from lesions. Methods: Patients with MS (18 RRMS, 11 SPMS), showing at least 2 CL, underwent 7T T2* imaging to obtain (1) magnitude images for segmenting focal intracortical lesion(s) (ICL) and leukocortical lesion(s) (LCL), and (2) cortical T2* maps. Anatomical scans were collected at 3T for cortical surface reconstruction using FreeSurfer. Seventeen age-matched healthy participants served as controls. Results: ICL were predominantly located in sulci of frontal, parietal, and cingulate cortex; LCL distribution was more random. In MS, T2* was higher in both ICL and LCL, indicating myelin and iron loss, than in NACGM (p , 0.00003) irrespective of CL subtype and MS phenotype. T2* was increased in perilesional cortex, tapering away from CL toward NACGM, the wider changes being for LCL in SPMS. NACGM T2* was higher in SPMS relative to RRMS (p 5 0.006) and healthy cortex (p 5 0.02). Conclusions: CL had the same degree of demyelination and iron loss regardless of lesion subtype and disease stage. Cortical damage expanded beyond visible CL, close to lesions in RRMS, and more diffusely in SPMS. Evaluation of NACGM integrity, beyond focal CL, could represent a surrogate marker of MS progression

Functional, anatomical and diffusion tensor MRI study of radiology expertise.

BackgroundRepeated practice to acquire expertise could result in the structural and functional changes in relevant brain circuits as a result of long-term potentiation, neurogenesis, glial genesis, and remodeling.PurposeThe goal of this study is to use task fMRI to study the brain of expert radiologists performing a diagnosis task where a series of medical images were presented during fMRI acquisition for 12s and participants were asked to choose a diagnosis. Structural and diffusion-tensor MRI were also acquired.MethodsRadiologists (N = 12, 11M, 38.2±10.3 years old) and non-radiologists (N = 17, 15M, 30.6±5.5 years old) were recruited with informed consent. Medical images were presented for 12 s and three multiple choices were displayed and the participants were asked to choose a diagnosis. fMRI, structural and diffusion-tensor MRI were acquired. fMRI analysis used FSL to determine differences in fMRI responses between groups. Voxel-wise analysis was performed to determine if subcortical volume, cortical thickness and fractional anisotropy differed between groups. Correction for multiple comparisons used false discovery rate.ResultsRadiologists showed overall lower task-related brain activation than non-radiologists. Radiologists showed significantly lower activation in the left lateral occipital cortex, left superior parietal lobule, occipital pole, right superior frontal and precentral gyri, lingual gyrus, and the left intraparietal sulcus (p0.05).ConclusionsRadiologists and non-radiologists had no significant difference in structural metrics. However, in diagnosis tasks, radiologists showed markedly lower task-related brain activations overall as well as a number of high-order visual and non-visual brain regions than non-radiologists. Some brain circuits appear to be uniquely associated with differential-diagnosis paradigm expertise that are not involved in simpler object-recognition cases. Improved understanding of the brain circuitry involved in acquisition of expertise might be used to design optimal training paradigms

The association between intra- and juxta-cortical pathology and cognitive impairment in multiple sclerosis by quantitative T2* mapping at 7 T MRI

Using quantitative T2* at 7 Tesla (T) magnetic resonance imaging, we investigated whether impairment in selective cognitive functions in multiple sclerosis (MS) can be explained by pathology in specific areas and/or layers of the cortex. Thirty-one MS patients underwent neuropsychological evaluation, acquisition of 7 T multi-echo T2* gradient-echo sequences, and 3 T anatomical images for cortical surfaces reconstruction. Seventeen age-matched healthy subjects served as controls. Cortical T2* maps were sampled at various depths throughout the cortex and juxtacortex. Relation between T2*, neuropsychological scores and a cognitive index (CI), calculated from a principal component analysis on the whole battery, was tested by a general linear model. Cognitive impairment correlated with T2* increase, independently from white matter lesions and cortical thickness, in cortical areas highly relevant for cognition belonging to the default-mode network (p < 0.05 corrected). Dysfunction in different cognitive functions correlated with longer T2* in selective cortical regions, most of which showed longer T2* relative to controls. For most tests, this association was strongest in deeper cortical layers. Executive dysfunction, however, was mainly related with pathology in juxtameningeal cortex. T2* explained up to 20% of the variance of the CI, independently of conventional imaging metrics (adjusted-R2: 52–67%, p < 5.10− 4). Location of pathology across the cortical width and mantle showed selective correlation with impairment in differing cognitive domains. These findings may guide studies at lower field strength designed to develop surrogate markers of cognitive impairment in MS

A gradient in cortical pathology in multiple sclerosis by in vivo quantitative 7 T imaging

We used a surface-based analysis of T2* relaxation rates at 7 T magnetic resonance imaging, which allows sampling quantitative T2* throughout the cortical width, to map in vivo the spatial distribution of intracortical pathology in multiple sclerosis. Ultrahigh resolution quantitative T2* maps were obtained in 10 subjects with clinically isolated syndrome/early multiple sclerosis (43 years disease duration), 18 subjects with relapsing-remitting multiple sclerosis (54 years disease duration), 13 subjects with secondary progressive multiple sclerosis, and in 17 age-matched healthy controls. Quantitative T2* maps were registered to anatomical cortical surfaces for sampling T2* at 25%, 50% and 75% depth from the pial surface. Differences in laminar quantitative T2* between each patient group and controls were assessed using general linear model (P50.05 corrected for multiple comparisons). In all 41 multiple sclerosis cases, we tested for associations between laminar quantitative T2*, neurological disability, Multiple Sclerosis Severity Score, cortical thickness, and white matter lesions. In patients, we measured, T2* in intracortical lesions and in the intracortical portion of leukocortical lesions visually detected on 7 T scans. Cortical lesional T2* was compared with patients’ normal-appearing cortical grey matter T2* (paired t-test) and with mean cortical T2* in controls (linear regression using age as nuisance factor). Subjects with multiple sclerosis exhibited relative to controls, independent from cortical thickness, significantly increased T2*, consistent with cortical myelin and iron loss. In early disease, T2* changes were focal and mainly confined at 25% depth, and in cortical sulci. In later disease stages T2* changes involved deeper cortical laminae, multiple cortical areas and gyri. In patients, T2* in intracortical and leukocortical lesions was increased compared with normal-appearing cortical grey matter (P51010 and P5107), and mean cortical T2* in controls (P5105 and P5106). In secondary progressive multiple sclerosis, T2* in normal-appearing cortical grey matter was significantly increased relative to controls (P50.001). Laminar T2* changes may, thus, result from cortical pathology within and outside focal cortical lesions. Neurological disability and Multiple Sclerosis Severity Score correlated each with the degree of laminar quantitative T2* changes, independently from white matter lesions, the greatest association being at 25% depth, while they did not correlate with cortical thickness and volume. These findings demonstrate a gradient in the expression of cortical pathology throughout stages of multiple sclerosis, which was associated with worse disability and provides in vivo evidence for the existence of a cortical pathological process driven from the pial surface

Machine learning in clinical neuroimaging

This book constitutes the refereed proceedings of the 6th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2023, held in Conjunction with MICCAI 2023 in Vancouver, Canada, in October 2023. The book includes 16 papers which were carefully reviewed and selected from 28 full-length submissions. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) aims to bring together the top researchers in both machine learning and clinical neuroscience as well as tech-savvy clinicians to address two main challenges: 1) development of methodological approaches for analyzing complex and heterogeneous neuroimaging data (machine learning track); and 2) filling the translational gap in applying existing machine learning methods in clinical practices (clinical neuroimaging track).The rise of neuroimaging data, bolstered by the rapid advancements in computational resources and algorithms, is poised to drive significant breakthroughs in clinical neuroscience. Notably, deep learning is gaining relevance in this domain. Yet, there’s an imbalance: while computational methods grow in complexity, the breadth and diversity of standard evaluation datasets lag behind. This mismatch could result in findings that don’t generalize to a wider population or are skewed towards dominant groups. To address this, it’s imperative to foster inter-domain collaborations that move state-of-the art methods quickly into clinical research. Bridging the divide between various specialties can pave the way for methodological innovations to smoothly transition into clinical research and ultimately, real-world applications.Ourworkshop aimed to facilitate this by creating a forum for dialogue among engineers, clinicians, and neuroimaging specialists. The 6th International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2023) was held on October 8th, 2023, as a satellite event of the 26th International Conference on Medical Imaging Computing & Computer-Assisted Intervention (MICCAI 2023) in Vancouver to continue the yearly recurring dialog between experts in machine learning and clinical neuroimaging. The call for papers was made on May 2nd, 2023, and submissions were closed on July 4th, 2023. Each of the 27 submitted manuscripts was reviewed by three or more program committee members in a double-blinded review process. The sixteen accepted papers showcase the integration of machine learning techniques with clinical neuroimaging data. Studied clinical conditions include Alzheimer’s disease, autism spectrum disorder, stroke, and aging. There is a strong emphasis on deep learning approaches to analysis of structural and functional MRI, positron emission tomography, and computed tomography. Research also delves into multi-modal data synthesis and analysis. The conference encapsulated the blend of methodological innovation and clinical applicability in neuroimaging. The proceedings mirror the hallmarks in the sections “Machine learning” and “Clinical applications”, although all papers carry clinical relevance and provide methodological novelty. For the sixth time, this workshop was put together by a dedicated community of authors, program committee, steering committee, and workshop participants. We thank all creators and attendees for their valuable contributions that made the MLCN 2023 Workshop a success

Development of an MRI-Compatible Nasal Drug Delivery Method for Probing Nicotine Addiction Dynamics

Substance abuse is a fundamentally dynamic disease, characterized by repeated oscillation between craving, drug self-administration, reward, and satiety. To model nicotine addiction as a control system, a magnetic resonance imaging (MRI)-compatible nicotine delivery system is needed to elicit cyclical cravings. Using a concentric nebulizer, inserted into one nostril, we delivered each dose equivalent to a single cigarette puff by a syringe pump. A control mechanism permits dual modes: one delivers puffs on a fixed interval programmed by researchers; with the other, subjects press a button to self-administer each nicotine dose. We tested the viability of this delivery method for studying the brain&rsquo;s response to nicotine addiction in three steps. First, we established the pharmacokinetics of nicotine delivery, using a dosing scheme designed to gradually achieve saturation. Second, we lengthened the time between microdoses to elicit craving cycles, using both fixed-interval and subject-driven behavior. Finally, we demonstrate a potential application of our device by showing that a fixed-interval protocol can reliably identify neuromodulatory targets for pharmacotherapy or brain stimulation. Our MRI-compatible nasal delivery method enables the measurement of neural circuit responses to drug doses on a single-subject level, allowing the development of data-driven predictive models to quantify individual dysregulations of the reward control circuit causing addiction