Current Understanding of the Anatomy, Physiology, and Magnetic Resonance Imaging of Neurofluids: Update From the 2022 "ISMRM Imaging Neurofluids Study group" Workshop in Rome.

Neurofluids is a term introduced to define all fluids in the brain and spine such as blood, cerebrospinal fluid, and interstitial fluid. Neuroscientists in the past millennium have steadily identified the several different fluid environments in the brain and spine that interact in a synchronized harmonious manner to assure a healthy microenvironment required for optimal neuroglial function. Neuroanatomists and biochemists have provided an incredible wealth of evidence revealing the anatomy of perivascular spaces, meninges and glia and their role in drainage of neuronal waste products. Human studies have been limited due to the restricted availability of noninvasive imaging modalities that can provide a high spatiotemporal depiction of the brain neurofluids. Therefore, animal studies have been key in advancing our knowledge of the temporal and spatial dynamics of fluids, for example, by injecting tracers with different molecular weights. Such studies have sparked interest to identify possible disruptions to neurofluids dynamics in human diseases such as small vessel disease, cerebral amyloid angiopathy, and dementia. However, key differences between rodent and human physiology should be considered when extrapolating these findings to understand the human brain. An increasing armamentarium of noninvasive MRI techniques is being built to identify markers of altered drainage pathways. During the three-day workshop organized by the International Society of Magnetic Resonance in Medicine that was held in Rome in September 2022, several of these concepts were discussed by a distinguished international faculty to lay the basis of what is known and where we still lack evidence. We envision that in the next decade, MRI will allow imaging of the physiology of neurofluid dynamics and drainage pathways in the human brain to identify true pathological processes underlying disease and to discover new avenues for early diagnoses and treatments including drug delivery. Evidence level: 1 Technical Efficacy: Stage 3.

Developing Topics.

BACKGROUND: The novel diffusion-based Neurite Orientation and Dispersion Imaging (NODDI) model may provide complementary information to Diffusion Tensor Imaging (DTI) to increase sensitivity in characterizing tissue microarchitecture. We examined if one or a combination of both approaches can improve our understanding of at-risk tissue in and around white matter hyperintensities (WMH). METHOD: From ADNI3, we identified individuals [n = 54, age = 78 ± 7, 46% F) with concurrent measures of multi-shell DTI, 3D FLAIR, and 3D MPRAGE. We segmented WMH from structural data using Lesion Segmentation Toolbox. We derived DTI (FA, MD) and NODDI parameters (NDI, ODI, Fiso) using QSIprep. We dilated up to 5 mm outside the WMH, applied an FA threshold of ≥ 0.2 to exclude gray matter, calculated DTI and NODDI parameters at 1 mm intervals within this region. We performed Pearson correlations between all parameters. Assuming normal white matter at 5 mm, we normalized these parameters with their respective values at 5 mm and plotted the spatial variation of each parameter in and outside the WMH. RESULT: Normalized DTI and NODDI parameters representing spatial variations within the 5 mm region are shown in Fig 1A. Table 1 shows p-values comparing DTI and NODDI parameters between WMH the subsequent regions outside it. Compared to WMH, Fiso was significantly different at all distances. FA showed similar spatial characteristics with NDI and was negatively correlated with ODI. Fiso correlated very strongly with MD. CONCLUSION: In the WMHs and its immediate vicinity, low FA, NDI, ODI suggest lower cell density, while low Fiso and MD suggest low extracellular fluid volume. Together these observations may reflect cytotoxic edema, cell loss, and injury. Normal appearing white matter (3-5 mm) showed high FA, NDI, ODI, and low MD consistent with high cell density and normal extracellular water. In the transition between WMH vicinity and normal white matter, low FA, NDI, ODI, high MD and Fiso suggest cell loss only. This transition region could be at risk of converting to WMH. Future work will include longitudinal evaluation of WMH growth in the at-risk tissue. However, final validation of WMH pathology will need histological confirmation.

Developing Topics.

BACKGROUND: Hippocampal and amygdala subfields variably affect cognitive impairment in neurodegenerative diseases. Subfields of these regions can be well segmented using modern neuroimaging tools but their role in neurodegenerative disease is under active investigation. In this study, we identified hippocampal and amygdala subregions predictive of cognitive performance and motor symptoms severity measured by MoCA (Montreal Cognitive Assessment) and MDS-UPDRS III, respectively, in patients with dementia with Lewy Bodies (DLB). METHOD: We selected all participants with probable DLB (N = 48, mean age = 71±7 years, 15% female) enrolled in the Dementia with Lewy Bodies Consortium (DLBC) as of July 2022, with concurrent measures of 3D T1 MRI sequence, MoCA, and MDS-UPDRS III scores. We performed cortical reconstruction and volumetric segmentation of hippocampal subfields and nuclei of the amygdala using FreeSurfer (v 7.2). We used combat harmonization to account for site and scanner differences. We trained and applied a bootstrapped, bidirectional stepwise regression model of 29 predictor variables comprised of sub-fields and mean cortical thickness against MoCA and MDS-UPDRS III, respectively, with an 80-20 train-test split ratio, and 5000 repetitions, corrected for age and sex. RESULT: Subfield segmentation is shown in Figure 1A. The best fitting model for MoCA included mean cortical thickness, parasubiculum, hippocampal and amygdala transition area, corticoamygdaloid transition area, and CA3 body (Figure 1B, adjusted R2 = 0.51). The best fitting model for MDS-UPDRS III included the cortical nucleus of the amygdala and CA1 body (Figure 1C, adjusted R2 = 0.22). This model was considered a poor fit. We considered MoCA for further analysis and closely predicted scores in our 20% partitioned test sample (Figure 2A, R2 = 0.38). CONCLUSION: We report model-based selection of hippocampal and amygdala subfields to predict MoCA scores in DLB. Atrophy in these regions has been associated with global cognitive deficit in mild cognitive impairment and Alzheimer disease cohorts. The model fit for MDS-UPDRS III scores was poor, providing evidence that these brain regions do not serve a role in motor control.

Comparison of Human Tissue Gadolinium Retention and Elimination between Gadoteridol and Gadobenate.

Background Linear gadolinium-based contrast agents (GBCAs) are known to be retained at higher levels of gadolinium than macro-cyclic GBCAs. However, very little is known regarding their relative elimination rates and retained fraction of injected gadolinium. Purpose To quantify and compare gadolinium retention and elimination rates in human brain tissue, skin, and bone obtained from cadavers exposed to single-agent administration of either gadoteridol (macrocyclic GBCA) or gadobenate dimeglumine (linear GBCA). Materials and Methods Autopsy cases from August 2014 to July 2019 of patients exposed to a single type of GBCA, either gadoteridol or gadobenate dimeglumine, either single or multiple doses, were included. Gadolinium levels in the brain, skin, and bone were analyzed with inductively coupled plasma mass spectrometry. Linear regression was used to compare gadolinium retention between agents and estimate elimination rates of the retained gadolinium using the time between last injection and death. Results Twenty-eight cadavers with gadoteridol exposure and nine with gadobenate dimeglumine exposure were identified (22 men; age range, 19-83 years). The median gadolinium retention of gadobenate dimeglumine was 3.0-6.5 times higher than that of gadoteridol in the brain (P < .02), 4.4 times higher in bone (P = .002), and 2.9 times higher in skin (P = .05). Gadolinium retention in the globus pallidus (GP), dentate nucleus (DN), white matter (WM), bone, and skin decreased with time elapsed from last administration to death in both the gadobenate dimeglumine (GP: -3% per twofold increase in time, P = .69; DN: -2%, P = .83; WM: -20%, P = .01; bone: -22%, P = .07; skin: -47%, P < .001) and gadoteridol (GP: -17%, P = .11; DN: -16%, P = .15; WM: -30%, P < .001; bone: -11%, P = .16; skin: -24%, P = .01) groups (P values for elimination are compared with a null hypothesis of no elimination). Conclusion The linear agent gadobenate dimeglumine retains several-fold higher levels of gadolinium in the brain and bone compared with the macrocyclic agent gadoteridol. Nonzero elimination of retained gadolinium was detected in the white matter and skin for both agents. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Tweedle in this issue.

Medical comorbidities and lower myelin content are associated with poor cognition in young adults with perinatally acquired HIV.

OBJECTIVE: Approximately 40% of adults living with HIV experience cognitive deficits. Little is known about the risk factors for cognitive impairment and its association with myelin content in young adults living with perinatally acquired HIV (YApHIV), which is assessed in our cross-sectional study. DESIGN: A prospective, observational cohort study. METHODS: All participants underwent an 11-test cognitive battery and completed medical and social history surveys. Cognitive impairment was defined as Z scores falling at least 1.5 SD below the mean in at least two domains. Twelve participants underwent myelin water imaging. Neuroimaging data were compared to age and sex-matched HIV-uninfected controls. Regression analyses were used to evaluate for risk factors of lower cognitive domain scores and association between myelin content and cognition in YApHIV. RESULTS: We enrolled 21 virally suppressed YApHIV across two sites in the United States. Ten participants (48%) met criteria for cognitive impairment. Participants with any non-HIV related medical comorbidity scored lower across multiple cognitive domains compared to participants without comorbidities. Myelin content did not differ between YApHIV and controls after adjusting for years of education. Lower cognitive scores were associated with lower myelin content in the cingulum and corticospinal tract in YApHIV participants after correcting for multiple comparisons. CONCLUSION: Poor cognition in YApHIV may be exacerbated by non-HIV related comorbidities as noted in older adults with horizontally acquired HIV. The corticospinal tract and cingulum may be vulnerable to the legacy effect of untreated HIV in infancy. Myelin content may be a marker of cognitive reserve in YApHIV.

Preliminary cross-sectional investigations into the human glymphatic system using multiple novel non-contrast MRI methods.

We discuss two potential non-invasive MRI methods to cross-sectionally study two distinct facets of the glymphatic system and its association with sleep and aging. We apply diffusion-based intravoxel incoherent motion (IVIM) imaging to evaluate pseudodiffusion coefficient, D*, or cerebrospinal fluid (CSF) movement across large spaces like the subarachnoid space (SAS). We also performed perfusion-based multi-echo, Hadamard encoded multi-delay arterial spin labeling (ASL) to evaluate whole brain cortical cerebral blood flow (CBF) and transendothelial exchange (Tex) of water from the vasculature into the perivascular space and parenchyma. Both methods were used in young adults (N=9, 6F, 23±3 years old) in the setting of sleep and sleep deprivation. To study aging, 10 older adults, (6F, 67±3 years old) were imaged after a night of normal sleep only and compared with the young adults. D* in SAS was significantly (p<0.05) lesser after sleep deprivation (0.014±0.001 mm2/s) than after normal sleep (0.016±0.001 mm2/s), but was unchanged with aging. Cortical CBF and Tex on the other hand, were unchanged after sleep deprivation but were significantly lower in older adults (37±3 ml/100g/min, 476±66 ms) than young adults (42±2 ml/100g/min, 624±66 ms). IVIM was thus, sensitive to sleep physiology and multi-echo, multi-delay ASL was sensitive to aging.

Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls.

Purpose: Conventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer's disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer's disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls. Methods: We used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies-Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05. Results: We identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04). Conclusion: We show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.