The neurovascular unit and its correlation with cognitive performance in patients with cerebral small vessel disease: a canonical correlation analysis approach.

Growing evidence indicates an important role of neurovascular unit (NVU) dysfunction in the pathophysiology of cerebral small vessel disease (cSVD). Individually measurable functions of the NVU have been correlated with cognitive function, but a combined analysis is lacking. We aimed to perform a unified analysis of NVU function and its relation with cognitive performance. The relationship between NVU function in the white matter and cognitive performance (both latent variables composed of multiple measurable variables) was investigated in 73 patients with cSVD (mean age 70 ± 10 years, 41% women) using canonical correlation analysis. MRI-based NVU function measures included (1) the intravoxel incoherent motion derived perfusion volume fraction (f) and microvascular diffusivity (D*), reflecting cerebral microvascular flow; (2) the IVIM derived intermediate volume fraction (fint), indicative of the perivascular clearance system; and (3) the dynamic contrast-enhanced MRI derived blood-brain barrier (BBB) leakage rate (Ki) and leakage volume fraction (VL), reflecting BBB integrity. Cognitive performance was composed of 13 cognitive test scores. Canonical correlation analysis revealed a strong correlation between the latent variables NVU function and cognitive performance (r 0.73; p = 0.02). For the NVU, the dominating variables were D*, fint, and Ki. Cognitive performance was driven by multiple cognitive tests comprising different cognitive domains. The functionality of the NVU is correlated with cognitive performance in cSVD. Instead of focusing on individual pathophysiological mechanisms, future studies should target NVU dysfunction as a whole to acquire a coherent understanding of the complex disease mechanisms that occur in the NVU in cSVD.Trial registration: NTR3786 (Dutch Trial Register).

Brain structure and connectivity mediate the association between lifestyle and cognition: The Maastricht Study.

Life-course exposure to risk and protective factors impacts brain macro- and micro-structure, which in turn affects cognition. The concept of brain-age gap assesses brain health by comparing an individual's neuroimaging-based predicted age with their calendar age. A higher BAG implies accelerated brain ageing and is expected to be associated with worse cognition. In this study, we comprehensively modelled mutual associations between brain health and lifestyle factors, brain age and cognition in a large, middle-aged population. For this study, cognitive test scores, lifestyle and 3T MRI data for n = 4881 participants [mean age (± SD) = 59.2 (±8.6), 50.1% male] were available from The Maastricht Study, a population-based cohort study with extensive phenotyping. Whole-brain volumes (grey matter, cerebrospinal fluid and white matter hyperintensity), cerebral microbleeds and structural white matter connectivity were calculated. Lifestyle factors were combined into an adapted LIfestyle for BRAin health weighted sum score, with higher score indicating greater dementia risk. Cognition was calculated by averaging z-scores across three cognitive domains (memory, information processing speed and executive function and attention). Brain-age gap was calculated by comparing calendar age to predictions from a neuroimaging-based multivariable regression model. Paths between LIfestyle for BRAin health tertiles, brain-age gap and cognitive function were tested using linear regression and structural equation modelling, adjusting for sociodemographic and clinical confounders. The results show that cerebrospinal fluid, grey matter, white matter hyperintensity and cerebral microbleeds best predicted brain-age gap (R 2 = 0.455, root mean squared error = 6.44). In regression analysis, higher LIfestyle for BRAin health scores (greater dementia risk) were associated with higher brain-age gap (standardized regression coefficient ß = 0.126, P < 0.001) and worse cognition (ß = -0.046, P = 0.013), while higher brain-age gap was associated with worse cognition (ß=-0.163, P < 0.001). In mediation analysis, 24.7% of the total difference in cognition between the highest and lowest LIfestyle for BRAin health tertile was mediated by brain-age gap (ß indirect = -0.049, P < 0.001; ß total = -0.198, P < 0.001) and an additional 3.8% was mediated via connectivity (ß indirect = -0.006, P < 0.001; ß total = -0.150, P < 0.001). Findings suggest that associations between health- and lifestyle-based risk/protective factors (LIfestyle for BRAin health) and cognition can be partially explained by structural brain health markers (brain-age gap) and white matter connectivity markers. Lifestyle interventions targeted at high-risk individuals in mid-to-late life may be effective in promoting and preserving cognitive function in the general public.

The incidence of neurological complications in mechanically ventilated COVID-19 ICU patients: An observational single-center cohort study in three COVID-19 periods.

BACKGROUND: Neurological complications in COVID-19 patients admitted to an intensive care unit (ICU) have been previously reported. As the pandemic progressed, therapeutic strategies were tailored to new insights. This study describes the incidence, outcome, and types of reported neurological complications in invasively mechanically ventilated (IMV) COVID-19 patients in relation to three periods during the pandemic. METHODS: IMV COVID-19 ICU patients from the Dutch Maastricht Intensive Care COVID (MaastrICCht) cohort were included in a single-center study (March 2020 - October 2021). Demographic, clinical, and follow-up data were collected. Electronic medical records were screened for neurological complications during hospitalization. Three distinct periods (P1, P2, P3) were defined, corresponding to periods with high hospitalization rates. ICU survivors with and without reported neurological complications were compared in an exploratory analysis. RESULTS: IMV COVID-19 ICU patients (n=324; median age 64 [IQR 57-72] years; 238 males (73.5%)) were stratified into P1 (n=94), P2 (n=138), and P3 (n=92). ICU mortality did not significantly change over time (P1=38.3%; P2=41.3%; P3=37.0%; p=.787). The incidence of reported neurological complications during ICU admission gradually decreased over the periods (P1=29.8%; P2=24.6%; P3=18.5%; p=.028). Encephalopathy/delirium (48/324 (14.8%)) and ICU-acquired weakness (32/324 (9.9%)) were most frequently reported and associated with ICU treatment intensity. ICU survivors with neurological complications (n=53) were older (p=.025), predominantly male (p=.037), and had a longer duration of IMV (p<.001) and ICU stay (p<.001), compared to survivors without neurological complications (n=132). A multivariable analysis revealed that only age was independently associated with the occurrence of neurological complications (ORadj=1.0541; 95% CI=1.0171-1.0925; p=.004). Health-related quality-of-life at follow-up was not significantly different between survivors with and without neurological complications (n = 82, p=.054). CONCLUSIONS: A high but decreasing incidence of neurological complications was reported during three consecutive COVID-19 periods in IMV COVID-19 patients. Neurological complications were related to the intensity of ICU support and treatment, and associated with prolonged ICU stay, but did not lead to significantly worse reported health-related quality-of-life at follow-up.

Results of the 2023 ISBI challenge to reduce GABA-edited MRS acquisition time.

PURPOSE: Use a conference challenge format to compare machine learning-based gamma-aminobutyric acid (GABA)-edited magnetic resonance spectroscopy (MRS) reconstruction models using one-quarter of the transients typically acquired during a complete scan. METHODS: There were three tracks: Track 1: simulated data, Track 2: identical acquisition parameters with in vivo data, and Track 3: different acquisition parameters with in vivo data. The mean squared error, signal-to-noise ratio, linewidth, and a proposed shape score metric were used to quantify model performance. Challenge organizers provided open access to a baseline model, simulated noise-free data, guides for adding synthetic noise, and in vivo data. RESULTS: Three submissions were compared. A covariance matrix convolutional neural network model was most successful for Track 1. A vision transformer model operating on a spectrogram data representation was most successful for Tracks 2 and 3. Deep learning (DL) reconstructions with 80 transients achieved equivalent or better SNR, linewidth and fit error compared to conventional 320 transient reconstructions. However, some DL models optimized linewidth and SNR without actually improving overall spectral quality, indicating a need for more robust metrics. CONCLUSION: DL-based reconstruction pipelines have the promise to reduce the number of transients required for GABA-edited MRS.

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task.

Introduction: Approximately one in six people will experience an episode of major depressive disorder (MDD) in their lifetime. Effective treatment is hindered by subjective clinical decision-making and a lack of objective prognostic biomarkers. Functional MRI (fMRI) could provide such an objective measure but the majority of MDD studies has focused on static approaches, disregarding the rapidly changing nature of the brain. In this study, we aim to predict depression severity changes at 3 and 6 months using dynamic fMRI features. Methods: For our research, we acquired a longitudinal dataset of 32 MDD patients with fMRI scans acquired at baseline and clinical follow-ups 3 and 6 months later. Several measures were derived from an emotion face-matching fMRI dataset: activity in brain regions, static and dynamic functional connectivity between functional brain networks (FBNs) and two measures from a wavelet coherence analysis approach. All fMRI features were evaluated independently, with and without demographic and clinical parameters. Patients were divided into two classes based on changes in depression severity at both follow-ups. Results: The number of coherence clusters (nCC) between FBNs, reflecting the total number of interactions (either synchronous, anti-synchronous or causal), resulted in the highest predictive performance. The nCC-based classifier achieved 87.5% and 77.4% accuracy for the 3- and 6-months change in severity, respectively. Furthermore, regression analyses supported the potential of nCC for predicting depression severity on a continuous scale. The posterior default mode network (DMN), dorsal attention network (DAN) and two visual networks were the most important networks in the optimal nCC models. Reduced nCC was associated with a poorer depression course, suggesting deficits in sustained attention to and coping with emotion-related faces. An ensemble of classifiers with demographic, clinical and lead coherence features, a measure of dynamic causality, resulted in a 3-months clinical outcome prediction accuracy of 81.2%. Discussion: The dynamic wavelet features demonstrated high accuracy in predicting individual depression severity change. Features describing brain dynamics could enhance understanding of depression and support clinical decision-making. Further studies are required to evaluate their robustness and replicability in larger cohorts.

DeepFLAIR: A neural network approach to mitigate signal and contrast loss in temporal lobes at 7 Tesla FLAIR images.

BACKGROUND AND PURPOSE: Higher magnetic field strength introduces stronger magnetic field inhomogeneities in the brain, especially within temporal lobes, leading to image artifacts. Particularly, T2-weighted fluid-attenuated inversion recovery (FLAIR) images can be affected by these artifacts. Here, we aimed to improve the FLAIR image quality in temporal lobe regions through image processing of multiple contrast images via machine learning using a neural network. METHODS: Thirteen drug-resistant MR-negative epilepsy patients (age 29.2 ± 9.4y, 5 females) were scanned on a 7 T MRI scanner. Magnetization-prepared (MP2RAGE) and saturation-prepared with 2 rapid gradient echoes, multi-echo gradient echo with four echo times, and the FLAIR sequence were acquired. A voxel-wise neural network was trained on extratemporal-lobe voxels from the acquired structural scans to generate a new FLAIR-like image (i.e., deepFLAIR) with reduced temporal lobe inhomogeneities. The deepFLAIR was evaluated in temporal lobes through signal-to-noise (SNR), contrast-to-noise (CNR) ratio, the sharpness of the gray-white matter boundary and joint-histogram analysis. Saliency mapping demonstrated the importance of each input image per voxel. RESULTS: SNR and CNR in both gray and white matter were significantly increased (p < 0.05) in the deepFLAIR's temporal ROIs, compared to the FLAIR. The gray-white matter boundary sharpness was either preserved or improved in 10/13 right-sided temporal regions and was found significantly increased in the ROIs. Multiple image contrasts were influential for the deepFLAIR reconstruction with the MP2RAGE second inversion image being the most important. CONCLUSIONS: The deepFLAIR network showed promise to restore the FLAIR signal and reduce contrast attenuation in temporal lobe areas. This may yield a valuable tool, especially when artifact-free FLAIR images are not available.

On the merits and potential of advanced neuroimaging techniques in COVID-19: A scoping review.

Many Coronavirus Disease 2019 (COVID-19) patients are suffering from long-term neuropsychological sequelae. These patients may benefit from a better understanding of the underlying neuropathophysiological mechanisms and identification of potential biomarkers and treatment targets. Structural clinical neuroimaging techniques have limited ability to visualize subtle cerebral abnormalities and to investigate brain function. This scoping review assesses the merits and potential of advanced neuroimaging techniques in COVID-19 using literature including advanced neuroimaging or postmortem analyses in adult COVID-19 patients published from the start of the pandemic until December 2023. Findings were summarized according to distinct categories of reported cerebral abnormalities revealed by different imaging techniques. Although no unified COVID-19-specific pattern could be subtracted, a broad range of cerebral abnormalities were revealed by advanced neuroimaging (likely attributable to hypoxic, vascular, and inflammatory pathology), even in absence of structural clinical imaging findings. These abnormalities are validated by postmortem examinations. This scoping review emphasizes the added value of advanced neuroimaging compared to structural clinical imaging and highlights implications for brain functioning and long-term consequences in COVID-19.

Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study.

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.

Microvascular Dysfunction and Whole-Brain White Matter Connectivity: The Maastricht Study.

BACKGROUND: Microvascular dysfunction is involved in the development of various cerebral disorders. It may contribute to these disorders by disrupting white matter tracts and altering brain connectivity, but evidence is scarce. We investigated the association between multiple biomarkers of microvascular function and whole-brain white matter connectivity. METHODS AND RESULTS: Cross-sectional data from The Maastricht Study, a Dutch population-based cohort (n=4326; age, 59.4±8.6 years; 49.7% women). Measures of microvascular function included urinary albumin excretion, central retinal arteriolar and venular calibers, composite scores of flicker light-induced retinal arteriolar and venular dilation, and plasma biomarkers of endothelial dysfunction (intercellular adhesion molecule-1, vascular cell adhesion molecule-1, E-selectin, and von Willebrand factor). White matter connectivity was calculated from 3T diffusion magnetic resonance imaging to quantify the number (average node degree) and organization (characteristic path length, global efficiency, clustering coefficient, and local efficiency) of white matter connections. A higher plasma biomarkers of endothelial dysfunction composite score was associated with a longer characteristic path length (ß per SD, 0.066 [95% CI, 0.017-0.114]) after adjustment for sociodemographic, lifestyle, and cardiovascular factors but not with any of the other white matter connectivity measures. After multiple comparison correction, this association was nonsignificant. None of the other microvascular function measures were associated with any of the connectivity measures. CONCLUSIONS: These findings suggest that microvascular dysfunction as measured by indirect markers is not associated with whole-brain white matter connectivity.

Cerebral Microvascular Perfusion Assessed in Elderly Adults by Spin-Echo Dynamic Susceptibility Contrast MRI at 7 Tesla.

Perfusion measures of the total vasculature are commonly derived with gradient-echo (GE) dynamic susceptibility contrast (DSC) MR images, which are acquired during the early passes of a contrast agent. Alternatively, spin-echo (SE) DSC can be used to achieve specific sensitivity to the capillary signal. For an improved contrast-to-noise ratio, ultra-high-field MRI makes this technique more appealing to study cerebral microvascular physiology. Therefore, this study assessed the applicability of SE-DSC MRI at 7 T. Forty-one elderly adults underwent 7 T MRI using a multi-slice SE-EPI DSC sequence. The cerebral blood volume (CBV) and cerebral blood flow (CBF) were determined in the cortical grey matter (CGM) and white matter (WM) and compared to values from the literature. The relation of CBV and CBF with age and sex was investigated. Higher CBV and CBF values were found in CGM compared to WM, whereby the CGM-to-WM ratios depended on the amount of largest vessels excluded from the analysis. CBF was negatively associated with age in the CGM, while no significant association was found with CBV. Both CBV and CBF were higher in women compared to men in both CGM and WM. The current study verifies the possibility of quantifying cerebral microvascular perfusion with SE-DSC MRI at 7 T.

Lower cerebral blood flow predicts cognitive decline in patients with vascular cognitive impairment.

INTRODUCTION: Chronic cerebral hypoperfusion is one of the assumed pathophysiological mechanisms underlying vascular cognitive impairment (VCI). We investigated the association between baseline cerebral blood flow (CBF) and cognitive decline after 2 years in patients with VCI and reference participants. METHODS: One hundred eighty-one participants (mean age 66.3 ± 7.4 years, 43.6% women) underwent arterial spin labeling (ASL) magnetic resonance imaging (MRI) and neuropsychological assessment at baseline and at 2-year follow-up. We determined the association between baseline global and lobar CBF and cognitive decline with multivariable regression analysis. RESULTS: Lower global CBF at baseline was associated with more global cognitive decline in VCI and reference participants. This association was most profound in the domain of attention/psychomotor speed. Lower temporal and frontal CBF at baseline were associated with more cognitive decline in memory. DISCUSSION: Our study supports the role of hypoperfusion in the pathophysiological and clinical progression of VCI. HIGHLIGHTS: Impaired cerebral blood flow (CBF) at baseline is associated with faster cognitive decline in VCI and normal aging. Our results suggest that low CBF precedes and contributes to the development of vascular cognitive impairment. CBF determined by ASL might be used as a biomarker to monitor disease progression or treatment responses in VCI.

Association of hippocampal subfield volumes with prevalence, course and incidence of depressive symptoms: The Maastricht Study.

BACKGROUND: Late-life depression has been associated with volume changes of the hippocampus. However, little is known about its association with specific hippocampal subfields over time. AIMS: We investigated whether hippocampal subfield volumes were associated with prevalence, course and incidence of depressive symptoms. METHOD: We extracted 12 hippocampal subfield volumes per hemisphere with FreeSurfer v6.0 using T1-weighted and fluid-attenuated inversion recovery 3T magnetic resonance images. Depressive symptoms were assessed at baseline and annually over 7 years of follow-up (9-item Patient Health Questionnaire). We used negative binominal, logistic, and Cox regression analyses, corrected for multiple comparisons, and adjusted for demographic, cardiovascular and lifestyle factors. RESULTS: A total of n = 4174 participants were included (mean age 60.0 years, s.d. = 8.6, 51.8% female). Larger right hippocampal fissure volume was associated with prevalent depressive symptoms (odds ratio (OR) = 1.26, 95% CI 1.08-1.48). Larger bilateral hippocampal fissure (OR = 1.37-1.40, 95% CI 1.14-1.71), larger right molecular layer (OR = 1.51, 95% CI 1.14-2.00) and smaller right cornu ammonis (CA)3 volumes (OR = 0.61, 95% CI 0.48-0.79) were associated with prevalent depressive symptoms with a chronic course. No associations of hippocampal subfield volumes with incident depressive symptoms were found. Yet, lower left hippocampal amygdala transition area (HATA) volume was associated with incident depressive symptoms with chronic course (hazard ratio = 0.70, 95% CI 0.55-0.89). CONCLUSIONS: Differences in hippocampal fissure, molecular layer and CA volumes might co-occur or follow the onset of depressive symptoms, in particular with a chronic course. Smaller HATA was associated with an increased risk of incident (chronic) depression. Our results could capture a biological foundation for the development of chronic depressive symptoms, and stresses the need to discriminate subtypes of depression to unravel its biological underpinnings.

Blood-Brain Barrier Disruption and Perivascular Spaces in Small Vessel Disease and Neurodegenerative Diseases: A Review on MRI Methods and Insights.

Perivascular spaces (PVS) and blood-brain barrier (BBB) disruption are two key features of cerebral small vessel disease (cSVD) and neurodegenerative diseases that have been linked to cognitive impairment and are involved in the cerebral waste clearance system. Magnetic resonance imaging (MRI) offers the possibility to study these pathophysiological processes noninvasively in vivo. This educational review provides an overview of the MRI techniques used to assess PVS functionality and BBB disruption. MRI-visible PVS can be scored on structural images by either (subjectively) counting or (automatically) delineating the PVS. We highlight emerging (diffusion) techniques to measure proxies of perivascular fluid and its movement, which may provide a more comprehensive understanding of the role of PVS in diseases. For the measurement of BBB disruption, we explain the most commonly used MRI technique, dynamic contrast-enhanced (DCE) MRI, as well as a more recently developed technique based on arterial spin labeling (ASL). DCE MRI and ASL are thought to measure complementary characteristics of the BBB. Furthermore, we describe clinical studies that have utilized these MRI techniques in cSVD and neurodegenerative diseases, particularly Alzheimer's disease (AD). These studies demonstrate the role of PVS and BBB dysfunction in these diseases and provide insight into the large overlap, but also into the differences between cSVD and AD. Overall, MRI techniques may provide valuable insights into the pathophysiological mechanisms underlying these diseases and have the potential to be used as markers for disease progression and treatment response. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study.

INTRODUCTION: The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration. METHODS: We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency). RESULTS: After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency. DISCUSSION: The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

Peripheral Pain Captured Centrally: Altered Brain Morphology on MRI in Small Fiber Neuropathy Patients With and Without an SCN9A Gene Variant.

The current study aims to characterize brain morphology of pain as reported by small fiber neuropathy (SFN) patients with or without a gain-of-function variant involving the SCN9A gene and compare these with findings in healthy controls without pain. The Neuropathic Pain Scale was used in patients with idiopathic SFN (N = 20) and SCN9A-associated SFN (N = 12) to capture pain phenotype. T1-weighted, structural magnetic resonance imaging (MRI) data were collected in patients and healthy controls (N = 21) to 1) compare cortical thickness and subcortical volumes and 2) quantify the association between severity, quality, and duration of pain with morphological properties. SCN9A-associated SFN patients showed significant (P < .017, Bonferroni corrected) higher cortical thickness in sensorimotor regions, compared to idiopathic SFN patients, while lower cortical thickness was found in more functionally diverse regions (eg, posterior cingulate cortex). SFN patient groups combined demonstrated a significant (Spearman's ρ = .44-.55, P = .005-.049) correlation among itch sensations (Neuropathic Pain Scale-7) and thickness of the left precentral gyrus, and midcingulate cortices. Significant associations were found between thalamic volumes and duration of pain (left: ρ = -.37, P = .043; right: ρ = -.40, P = .025). No associations were found between morphological properties and other pain qualities. In conclusion, in SCN9A-associated SFN, profound morphological alterations anchored within the pain matrix are present. The association between itch sensations of pain and sensorimotor and midcingulate structures provides a novel basis for further examining neurobiological underpinnings of itch in SFN. PERSPECTIVE: Cortical thickness and subcortical volume alterations in SFN patients were found in pain hubs, more profound in SCN9A-associated neuropathy, and correlated with itch and durations of pain. These findings contribute to our understanding of the pathophysiological pathways underlying chronic neuropathic pain and symptoms of itch in SFN.

The added value of risk assessment and subsequent targeted treatment for epileptic seizures after stroke: An early-HTA analysis.

INTRODUCTION: The development of post-stroke epilepsy (PSE) is related to a worse clinical outcome in stroke patients. Adding a biomarker to the clinical diagnostic process for the prediction of PSE may help to establish targeted and personalized treatment for high-risk patients, which could lead to improved patient outcomes. We assessed the added value of a risk assessment and subsequent targeted treatment by conducting an early Health Technology Assessment. METHODS: Interviews were conducted with four relevant stakeholders in the field of PSE to obtain a realistic view of the current healthcare and their opinions on the potential value of a PSE risk assessment and subsequent targeted treatment. The consequences on quality of life and costs of current care of a hypothetical care pathway with perfect risk assessment were modeled based on information from a literature review and the input from the stakeholders. Subsequently, the maximum added value (the headroom) was calculated. Sensitivity analyses were performed to test the robustness of this result to variation in assumed input parameters, i.e. the accuracy of the risk assessment, the efficacy of anti-seizure medication (ASM), and the probability of patients expected to develop PSE. RESULTS: All stakeholders considered the addition of a predictive biomarker for the risk assessment of PSE to be of value. The headroom amounted to €12,983. The sensitivity analyses demonstrated that the headroom remained beneficial when varying the accuracy of the risk assessment, the ASM efficacy, and the number of patients expected to develop PSE. DISCUSSION: We showed that a risk assessment for PSE development is potentially valuable. This work demonstrates that it is worthwhile to undertake clinical studies to evaluate biomarkers for the prediction of patients at high risk for PSE and to assess the value of targeted prophylactic treatment.

Associations of an individual's need for cognition with structural brain damage and cognitive functioning/impairment: cross-sectional population-based study.

BACKGROUND: High cognitive activity possibly reduces the risk of cognitive decline and dementia. AIMS: To investigate associations between an individual's need to engage in cognitively stimulating activities (need for cognition, NFC) and structural brain damage and cognitive functioning in the Dutch general population with and without existing cognitive impairment. METHOD: Cross-sectional data were used from the population-based cohort of the Maastricht Study. NFC was measured using the Need For Cognition Scale. Cognitive functioning was tested in three domains: verbal memory, information processing speed, and executive functioning and attention. Values 1.5 s.d. below the mean were defined as cognitive impairment. Standardised volumes of white matter hyperintensities (WMH), cerebrospinal fluid (CSF) and presence of cerebral small vessel disease (CSVD) were derived from 3T magnetic resonance imaging. Multiple linear and binary logistic regression analyses were used adjusted for demographic, somatic and lifestyle factors. RESULTS: Participants (n = 4209; mean age 59.06 years, s.d. = 8.58; 50.1% women) with higher NFC scores had higher overall cognition scores (B = 0.21, 95% CI 0.17-0.26, P < 0.001) and lower odds for CSVD (OR = 0.74, 95% CI 0.60-0.91, P = 0.005) and cognitive impairment (OR = 0.60, 95% CI 0.48-0.76, P < 0.001) after adjustment for demographic, somatic and lifestyle factors. The association between NFC score and cognitive functioning was similar for individuals with and without prevalent cognitive impairment. We found no significant association between NFC and WMH or CSF volumes. CONCLUSIONS: A high need to engage in cognitively stimulating activities is associated with better cognitive functioning and less presence of CSVD and cognitive impairment. This suggests that, in middle-aged individuals, motivation to engage in cognitively stimulating activities may be an opportunity to improve brain health.

Exploring the core network of the structural covariance network in childhood absence epilepsy.

Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN). In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated. A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions. The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.

Imaging Interstitial Fluid With MRI: A Narrative Review on the Associations of Altered Interstitial Fluid With Vascular and Neurodegenerative Abnormalities.

Interstitial fluid (ISF) refers to the fluid between the parenchymal cells and along the perivascular spaces (PVS). ISF plays a crucial role in delivering nutrients and clearing waste products from the brain. This narrative review focuses on the use of MRI techniques to measure various ISF characteristics in humans. The complementary value of contrast-enhanced and noncontrast-enhanced techniques is highlighted. While contrast-enhanced MRI methods allow measurement of ISF transport and flow, they lack quantitative assessment of ISF properties. Noninvasive MRI techniques, including multi-b-value diffusion imaging, free-water-imaging, T2 -decay imaging, and DTI along the PVS, offer promising alternatives to derive ISF measures, such as ISF volume and diffusivity. The emerging role of these MRI techniques in investigating ISF alterations in neurodegenerative diseases (eg, Alzheimer's disease and Parkinson's disease) and cerebrovascular diseases (eg, cerebral small vessel disease and stroke) is discussed. This review also emphasizes current challenges of ISF imaging, such as the microscopic scale at which ISF has to be measured, and discusses potential focus points for future research to overcome these challenges, for example, the use of high-resolution imaging techniques. Noninvasive MRI methods for measuring ISF characteristics hold significant potential and may have a high clinical impact in understanding the pathophysiology of neurodegenerative and cerebrovascular disorders, as well as in evaluating the efficacy of ISF-targeted therapies in clinical trials. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.