Keywords: Alzheimer's Disease, Modelling, Virtual Brain modelling, biomarkers, brain dynamics, excitatory/inhibitory balance

Motivation: The high level of heterogeneity typical of mild cognitive impairment (MCI) condition currently hinders the selection of a personalized effective therapy.

Goal(s): Our goal is to obtain a personalized profile exploring not only structural and functional topology but also diving in subject-specific physiological parameters.

Approach: Starting from structural and functional connectomes, we combined graph theoretical analysis with virtual brain models in the default mode network of healthy subjects, MCI and Alzheimer's disease patients.

Results: Our results offer a detailed description of alterations at single-subject level, illustrating differences between dementia stages based on topology and subject-specific physiological parameters.

Impact: The personalized profile obtained combining graph theory and virtual brain models portray dementia stages at single-subject level, capturing the wide heterogeneity of mild cognitive impairment and opening new perspectives for personalized effective interventions.

Keywords: Alzheimer's Disease, Alzheimer's Disease, Transcriptomic, 5xFAD

Motivation: While numerous studies noted the reduction of fractional anisotropy (FA) from diffusion MRI as a sensitive marker in Alzheimer's disease, the underlying biology remained elusive.

Goal(s): We aimed to explore biological basis of DTI metrics by integrating diffusion MRI with spatial transcriptomic data from the same individual.

Approach: We performed voxelwise correlation between the co-registered transcriptomic and MRI data and downstream enrichment analysis.

Results: We revealed links to myelin, oligodendrocytes, and Alzheimer's-associated biological processes.These findings enhanced our understanding of changes of diffusion MRI in Alzheimer's disease.

Impact: Spatial imaging-transcriptomic provides a certain level of biological evidence for the molecular processes underlying DTI signatures of Alzheimer’s disease. Similar approach can be applied to other types of MRI markers in different neurodegenerative diseases.

Keywords: Alzheimer's Disease, Alzheimer's Disease, DTI, PET

Motivation: There is currently a profound need for non-invasive early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD).

Goal(s): The purpose of this study is to determine the relationships between imaging and cognitive testing metrics.

Approach: Subjects underwent multimodal imaging, including 7T DTI, 7T MRE, and amyloid PET, and a PACC test. These metrics were used in Shapley Regressions to determine which metrics were the best predictors of MRE or SUVR.

Results: We determined that SUVR was the best predictors of MRE metrics, and that MRE was one of the best predictors of SUVR in subjects with amyloidosis.

Impact: Using multimodal imaging at ultrahigh field (7T) we have observed preliminary relationships between amyloid deposition and biomechanical and microstructural metrics as determined by 7T MR Elastography and DTI in the human brain.

Keywords: Alzheimer's Disease, Alzheimer's Disease

Motivation: The decomposed high-frequency conductivity (HFC) into extra-neurite and intra-neurite components to calculate compartmental conductivities has not been applied to any neurological conditions.

Goal(s): To investigate how the separated extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer’s disease (AD) patients and to evaluate the association between compartmental conductivities and cognitive decline

Approach: A total 66 patients  included in 20 AD patients, 25 amnestic MCI patients, and 21 controls were scanned with a multi-echo turbo spin-echo and multi-shell diffusion tensor EPI sequences.

Results: The EC value was higher in patients with AD than others and decreased with increasing K-MMSE scores.

Impact: The EC value might be used as an imaging biomarker for helping to monitor cognitive function.

Keywords: Diagnosis/Prediction, Multimodal, Generalization; Generalizable; Longitudinal; Disease progression modeling

Motivation: Current longitudinal AD-dementia progression prediction studies lack cross-cohort evaluation, raising concerns about the clinical applicability of prediction models.

Goal(s): Our goal was to develop a generalizable ML algorithm, L2C-FNN, and assess its generalizability across entirely distinct test cohorts.

Approach: L2C-FNN and baseline models were trained solely on ADNI and subsequently evaluated on AIBL, MACC, and OASIS. Multimodal biomarkers were leveraged for forecasting future clinical diagnosis, cognition, and ventricle volume.

Results: Our algorithm compares favorably against strong baseline models across all test datasets, confirming its superior generalizability.

Impact: The demonstrated potential for improved generalizability in L2C-FNN signifies progress toward enhancing AI prediction models for clinical application. This underscores the continued need for cross-cohort evaluation in future AD-dementia progression modeling studies.

Keywords: Hyperpolarized MR (Non-Gas), Alzheimer's Disease, Metabolism, Hyperpolarized MR, Proton MRS

Motivation: As metabolic impairment is key in AD, metabolic imaging could potentially improve diagnosis and monitoring of AD. 

Goal(s): Our goal is to determine which metabolic imaging approach, or combination of approaches, provide the optimal set of biomarkers for AD. 

Approach: We combined three metabolic imaging methods, ¹H MRS, HP ¹³C MRSI and ¹⁸F-FDG PET, with machine learning to characterize the neurometabolic profiles linked to AD-related risk factors, namely ApoE mutation, APP mutation, and sex in AD mouse models. 

Results: Combining metabolic neuroimaging and machine learning can help discriminate between AD-related mutational status (APP and ApoE) and provide information of AD-related sexual dimorphism.

Impact: Knowing which metabolic imaging approach(es) is/are optimal to monitor progression in each subset of AD patients, based on sex and mutational status, would improve patient-centric clinical care and potentially create new avenues for assessment of new metabolism-targeting therapies.

Keywords: Alzheimer's Disease, Neurodegeneration

Motivation: Validating pathological findings from ultra-high-resolution ex-vivo MRI through histology is significant but challenging due to nonlinear 3D deformations between MRI and histological samples.

Goal(s): Addressing the challenge of accurately quantifying complex neurodegenerative diseases by improving the alignment of post-mortem MRI data with histological images.

Approach: We built a novel pipeline integrating advanced imaging techniques with innovative registration algorithms, linking high-resolution MRI with blockface imaging and histological sections.

Results: Our methodology successfully generated blockface volumes with minimal distortion and artifacts, accomplished precise alignment between MRI and blockface volumes, and achieved an accurate 2D correspondence between MRI and histology slides.

Impact: This study introduces an advanced correlative MRI-histology pipeline with robust 2D and 3D coregistration methods, promising to enhance our understanding of neurodegenerative diseases and contribute to the evolution of MRI-based biomarkers for the disease.

Keywords: Parkinson's Disease, Parkinson's Disease

Motivation: Neuromelanin is a pigment that accumulates specifically in neurons population that are vulnerable in Parkinson's disease. The role of neuromelanin in pathogenesis is still unclear.

Goal(s): We tested the hypothesis that there is a pathogenic threshold of neuromelanin accumulation that triggers neurodegeneration in Parkinson's disease patients.

Approach: We performed longitudinal neuromelanin-MRI imaging of the substantia nigra of Parkinson's disease, prodromal Parkinson's disease (iRBD) patients, and healthy volunteers. 

Results: We confirmed accelerated decrease of neuromelanin-MRI signal in patients with Parkinson's disease, which started from the maximum of healthy volunteer, in line with hypothetic pathogenic threshold. iRBD patients showed similar trajectory delayed by 5 years.

Impact: Results support the hypothesis of a pathogenic threshold of neuromelanin. Its role in Parkinson's disease pathogenesis needs more investigations. Late reach of this threshold in prodromal patients results in delayed age of Parkinson's disease onset, suggesting different progression pattern.

Keywords: Parkinson's Disease, Parkinson's Disease

Motivation: The involvement of the brain’s sensory systems in PD is poorly understood and visual deficits are often a complex and underappreciated aspect of the disease.

Goal(s): The goal of this study is to study the visual deficits in the PD mouse line by using fMRI, C-FOS expression and CBF.

Approach: Here we report aberrations in BOLD-fMRI responses along the visual pathway in mouse model of PD and validate via C-FOS protein expression and ASL. 

Results: Our findings revealed decreased activity in the visual areas, decreased C-FOS confirmed the neural origin, and the ASL excluded any vascular differences that could alter the fMRI signals. 
 

Impact: Many individuals with PD experience a decline in visual acuity. Thus, understanding and addressing visual deficits in Parkinson's disease is crucial for improving the overall well-being and daily functioning of individuals living with this condition.

Keywords: CEST / APT / NOE, CEST & MT, Z-spectrum fitting

Motivation: Achieving precise quantification of target molecules has been a prominent focus of chemical exchange saturation transfer (CEST) .

Goal(s): To investigate the alteration of CEST-MRI in the bilateral substantia nigra (SN) and red nucleus (RN) in Parkinson’s disease (PD) and to explore its value of clinical application.

Approach: The signal change of CEST imaging was separated using the 4-pool Lorentz fitting model. The amide, nuclear overhauser enhancement (NOE), direct water saturation (DS), and magnetization transfer (MT) value were compared between the PD and NC group.

Results: The results indicated that CEST-MR can reveal the signal alterations the SN and RN in PD patients.

Impact: CEST-MRI, utilizing the 4-pool Lorentz fitting model, was employed to accurately delineate the amide signal alterations within the SN and RN of patients with PD. This approach demonstrates significant promise for enhancing the clinical diagnosis of PD.

Keywords: Aging, Brain Connectivity, fMRI Analysis, functional connectivity, neuroscience, fMRI (task-based)

Motivation:  Presently, olfactory dysfunctions such as with aging, neurodegenerative diseases and COVID-19 remain poorly understood at the systems level despite extensive knowledge of the microcircuit changes at the olfactory bulb (OB). 

Goal(s): We aim to reveal the systematic abnormalities of downstream olfactory information processing from the OB in prematurely aged rats. 

Approach: We examined the effective connectivity of olfactory networks in both healthy and aged rat models with optogenetic fMRI and dynamic causal modeling. 

Results: We demonstrate that network-specific dynamics in the olfactory system between aged and healthy rats could be attributed to altered effective connectivity driven by primary olfactory regions downstream from OB.

Impact: The ability to stimulate olfactory bulb excitatory neurons and model the downstream neural activity dynamics at the system level in healthy and aged animals has revealed key regions that are involved in olfactory dysfunctions, which can guide future therapeutic interventions. 

Keywords: Aging, Ischemia

Motivation: Understanding the magnitude and threshold of PVWMH and DWMH that disrupt cognitive abilities in MCI and AD, compared to healthy aging.

Goal(s): To investigate white matter hyperintensity distribution & its impact on cognitive functions.

Approach: Neuroanatomic segmentation & quantification of Periventricular WMH and DeepWMH, with mediation analysis assessing their impact on cognitive functions

Results: WMH load accrues vascular insult to brain structures, which in-turn mediates impaired cognitive functions, specifically motor and executive functions. WMH load in periventricular region abrogates the information processing and processing speed indirectly mediated through paracentral gyrus thickness, rostral middle frontal volume and lingual gyrus thickness.

Impact: Periventricular white matter hyperintensity progresses faster compared to DeepWMH with Aging. We establish that the Regional Distribution of DeepWMH load is distinct for CN, MCI and AD. High WMH load impairs Executive memory and Motor Memory via specific structural atrophy.

Keywords: Multiple Sclerosis, fMRI

Motivation: Both Alzheimer’s disease (AD) and multiple sclerosis (MS) patients exhibit brain atrophy driven cognitive impairment.

Goal(s): To identify the specific and common regions in GMV reduction in AD and MS and genetic basis associated with volume changes.

Approach: VBM meta-analyses and conjunction analyses were performed for comparison. GMV associated gene expression data were extracted from Allen Human Brain Atlas by cross-sample partial least squares regression.

Results: MS patients have reduced thalamic volume, while AD have hippocampal atrophy. Both MS and AD patients exhibit medial temporal lobe atrophy patterns, which were associated with 843 genes in functioning at biological processes, neurons, and immune cells.

Impact: MS and AD patients have specific and common patterns of gray matter volume reduction, given a neuroimage clue that the ageing population present with similar symptoms of cognitive impairment.

Keywords: Other Neurodegeneration, Relaxometry, Demyelination, remyelination, RAFF4, MT, DTI

Motivation: In vivo assessment of myelin status is important for diagnostic and therapeutic purposes in multiple sclerosis.

Goal(s): The goal of this study was to explore the capability of RAFF4, MT, and DTI metrics to detect changes in the myelin content and integrity during both demyelination and remyelination.

Approach: A genetic mouse model of widespread demyelination and remyelination was imaged with RAFF4, MT, and DTI and the MRI metrics were compared with histological analyses.

Results: Both RAFF4 and MT detected differences between the disease model and control animals in both demyelination and remyelination. DTI differed only in the demyelination phase. 

Impact: RAFF4 showed the ability to detect both demyelination and remyelination in the mouse brain. This suggests that RAFF4 has great potential in serving as a translational biomarker in the development of new therapeutic agents for myelin repair.

Keywords: Multiple Sclerosis, Neurodegeneration, High-Field MRI, Diffusion Modelling, Tissue Characterization

Motivation: Cortical lesions are linked to irreversible cortical atrophy as well as cognitive impairment in multiple sclerosis. High-gradient diffusion MRI is sensitive to the microstructural substrate of neurodegeneration in multiple sclerosis.

Goal(s): To identify in-vivo patterns of cell body density alterations, quantified by advanced diffusion MRI, in and surrounding focal cortical demyelination in people with multiple sclerosis.

Approach: The intra-cellular signal fraction, reflective of cell body density, was compared between cortical lesions, perilesional and normal-appearing cortex.

Results: Multiple sclerosis-related decreases in intra-cellular signal fraction were seen in cortical lesions compared to perilesional and normal-appearing cortex.

Impact: High-gradient diffusion MRI has the potential to identify cortical cell body loss in-vivo, potentially attributable to focal demyelination, relevant for cognition.

Keywords: Gray Matter, Multiple Sclerosis

Motivation: Patterns of cortical microstructural damage in multiple sclerosis (MS) can be examined in vivo with high-resolution diffusion tensor imaging (DTI).

Goal(s): To assess cortical diffusion changes in MS across the lifespan.

Approach: High-resolution DTI from controls (5-74 years) and MS participants (13-72 years) were segmented using an only-DTI-based method. Thickness, standard DTI metrics and radiality were evaluated in the entire cortex in MS against normative development/aging.

Results: Cortical changes were observed in ~1/3 of MS participants versus controls over the entire lifespan, such as thinning, higher mean (MD), axial (AD) and radial (RD) diffusivities, and lower radiality.

Impact: This study highlights microstructural abnormalities in the cortex of multiple sclerosis (MS) patients throughout the lifespan. These findings will help to understand in vivo cortical pathology in MS that might precede atrophy and that could be linked with disease progression/phenotypes.

Keywords: Neuroinflammation, Brain Connectivity, Cerebral Metabolic Rate of Oxygen

Motivation: Obstructive sleep apnea (OSA) is a common disorder predisposing patients to heart disease, stroke, and cognitive dysfunction.

Goal(s): To gain insights into the association between brain metabolism and changes in upper airway architecture during spontaneous apneas during sleep in the scanner.

Approach: A time-resolved pulse sequence was designed that yields neurometabolic parameters and airway anatomy at 6-second temporal resolution, along with EEG monitoring during a 90-minute scan.

Results: Data demonstrate associations between transient airway architectural changes and brain vascular-metabolic alterations, notably a steep drop in cerebral metabolic rate of oxygen (CMRO₂) during sleep and following apneic events, providing new insight into the disorder.    

Impact: Understanding the acute structural and neurometabolic consequences of apneic events in obstructive sleep apnea will provide new insight into the disease and provide a method to evaluate the response to treatment.

Keywords: Data Processing, COVID-19, statistical models, clinical scores, fatigue, anosmia, cognitive impairment, multimodal qMRI

Motivation: Long-COVID is a disabling health problem caused by SARS-COV-2 syndrome, whose underlying biological mechanisms are still debated. 

Goal(s): This study aimed at finding the set of quantitative MRI (qMRI) metrics that best correlate with fatigue, smell (i.e. anosmia),and cognitive dysfunction, common in this condition.

Approach: People with COVID19 history with and without long-COVID were assessed through a multimodal one-hour-long qMRI protocol and underwent clinical evaluation. 

Results: Correlation analyses between qMRI metrics and clinical scores showed that neurite density index changes explain both fatigue and smell function (also affected by changes in brain stem volume),while mean diffusivity and magnetic susceptibility changes explain cognitive function.

Impact: This work sheds light on the underlying biological mechanisms of long-COVID (anosmia, fatigue, and cognitive impairment). Metrics sensitive to microstructure, inflammation and possible iron accumulation best explain persistent symptoms, emphasizing the role of multimodal qMRI in the clinic.

Keywords: Multiple Sclerosis, Multiple Sclerosis

Motivation: Conventional MRI struggles to capture heterogeneous histopathological subtypes within multiple sclerosis (MS) lesions, mainly due to a lack of microstructural specificity.

Goal(s): (i) To unveil distinct subtypes of microstructural alteration MS lesions using advanced multi-contrast microstructural MRI; (ii) increase sensitivity to individual microstructure.

Approach: K-means clustering was applied to multi-contrast microstructural MRI quantities, including parameters from diffusometry (μFA [axonal integrity marker], MD), susceptometry (QSM, 𝜒_dia [demyelination marker] 𝜒_para [marker for iron-laden microglia]), and relaxometry (R2*, R2, T1).

Results: Five MRI-driven lesion subtypes, each with unique microstructural property combinations, revealed potential histopathological features of MS lesions and showed enhanced sensitivities to clinical outcomes.

Impact: We used a novel imaging multi-biomarker for in-vivo MS pathology to assess lesion types for potential treatment monitoring in MS. Some MS subtypes with microstructure alterations, potentially related to disease histopathology, showed improved clinical sensitivity over conventional imaging markers.

Keywords: Multiple Sclerosis, Metabolism, cerebral metabolism, neural stimulation. blood flow

Motivation: The cerebral metabolic underpinnings of tDCS, both during the stimulation itself and as result of repeated sessions are still not fully understood.

Goal(s): To quantify the immediate tDCS effects (simultaneous) using real-time tDCS-MRI and treatment-related effects (cumulative after repeated sessions) in multiple sclerosis (MS) patients. 

Approach: MS patients had tDCS-MRI performed at baseline and after 20 tDCS treatment sessions. Imaging measurements were acquired pre-, during- (2.0mA left frontal anodal) and post-tDCS. 

Results: During tDCS, at baseline, we observed a 7.6% increase in cerebral metabolic rate of oxygen (CMRO₂). tDCS-treatment induced a 9.6% increase of the pre-tDCS CMRO₂ levels.

Impact: The significant increase in neuronal metabolism following both real-time and repeated tDCS treatment in MS patients offers valuable insights into the biophysiological mechanisms regarding acute and cumulative tDCS effects, informing future clinical applications in MS and other neurodegenerative diseases.