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

IntroductionApproximately 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.MethodsFor 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.ResultsThe 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%.DiscussionThe 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

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github. com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc

A Comprehensive View on MRI Techniques for Imaging Blood-Brain Barrier Integrity

The blood-brain barrier (BBB) is the interface between the blood and brain tissue, which regulates the maintenance of homeostasis within the brain. Impaired BBB integrity is increasingly associated with various neurological diseases. To gain a better understanding of the underlying processes involved in BBB breakdown, magnetic resonance imaging (MRI) techniques are highly suitable for noninvasive BBB assessment. Commonly used MRI techniques to assess BBB integrity are dynamic contrast-enhanced and dynamic susceptibility contrast MRI, both relying on leakage of gadolinium-based contrast agents. A number of conceptually different methods exist that target other aspects of the BBB. These alternative techniques make use of endogenous markers, such as water and glucose, as contrast media. A comprehensive overview of currently available MRI techniques to assess the BBB condition is provided from a scientific point of view, including potential applications in disease. Improvements that are required to make these techniques clinically more easily applicable will also be discussed.</p

Prediabetes Is Associated With Structural Brain Abnormalities:The Maastricht Study

OBJECTIVE Structural brain abnormalities are key risk factors for brain diseases, such as dementia, stroke, and depression, in type 2 diabetes. It is unknown whether structural brain abnormalities already occur in prediabetes. Therefore, we investigated whether both prediabetes and type 2 diabetes are associated with lacunar infarcts (LIs), white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and brain atrophy. RESEARCH DESIGN and METHODS We used data from 2,228 participants (1,373 with normal glucose metabolism [NGM], 347 with prediabetes, and 508 with type 2 diabetes (oversampled); mean age 59.2 6 8.2 years; 48.3% women) of the Maastricht Study, a population-based cohort study. Diabetes status was determined with an oral glucose tolerance test. Brain imaging was performed with 3 Tesla MRI. Results were analyzed with multivariable logistic and linear regression analyses. RESULTS Prediabetes and type 2 diabetes were associated with the presence of LIs (odds ratio 1.61 [95% CI 0.98-2.63] and 1.67 [1.04-2.68], respectively; P trend = 0.027), larger WMH (b 0.07 log10-transformed mL [log-mL] [95% CI 0.00-0.15] and 0.21 log-mL [0.14-0.28], respectively; P trend <0.001), and smaller white matter volumes (b 24.0 mL [27.3 to 20.6] and 27.2 mL [210.4 to 24.0], respectively; P trend <0.001) compared with NGM. Prediabetes was not associated with gray matter volumes or the presence of CMBs. CONCLUSIONS Prediabetes is associated with structural brain abnormalities, with further deterioration in type 2 diabetes. These results indicate that, in middle-aged populations, structural brain abnormalities already occur in prediabetes, which may suggest that the treatment of early dysglycemia may contribute to the prevention of brain diseases

Hypoxic oligodendrocyte precursor cell-derived VEGFA is associated with blood–brain barrier impairment

Abstract Cerebral small vessel disease is characterised by decreased cerebral blood flow and blood–brain barrier impairments which play a key role in the development of white matter lesions. We hypothesised that cerebral hypoperfusion causes local hypoxia, affecting oligodendrocyte precursor cell—endothelial cell signalling leading to blood–brain barrier dysfunction as an early mechanism for the development of white matter lesions. Bilateral carotid artery stenosis was used as a mouse model for cerebral hypoperfusion. Pimonidazole, a hypoxic cell marker, was injected prior to humane sacrifice at day 7. Myelin content, vascular density, blood–brain barrier leakages, and hypoxic cell density were quantified. Primary mouse oligodendrocyte precursor cells were exposed to hypoxia and RNA sequencing was performed. Vegfa gene expression and protein secretion was examined in an oligodendrocyte precursor cell line exposed to hypoxia. Additionally, human blood plasma VEGFA levels were measured and correlated to blood–brain barrier permeability in normal-appearing white matter and white matter lesions of cerebral small vessel disease patients and controls. Cerebral blood flow was reduced in the stenosis mice, with an increase in hypoxic cell number and blood–brain barrier leakages in the cortical areas but no changes in myelin content or vascular density. Vegfa upregulation was identified in hypoxic oligodendrocyte precursor cells, which was mediated via Hif1α and Epas1. In humans, VEGFA plasma levels were increased in patients versus controls. VEGFA plasma levels were associated with increased blood–brain barrier permeability in normal appearing white matter of patients. Cerebral hypoperfusion mediates hypoxia induced VEGFA expression in oligodendrocyte precursor cells through Hif1α/Epas1 signalling. VEGFA could in turn increase BBB permeability. In humans, increased VEGFA plasma levels in cerebral small vessel disease patients were associated with increased blood–brain barrier permeability in the normal appearing white matter. Our results support a role of VEGFA expression in cerebral hypoperfusion as seen in cerebral small vessel disease

Association of Type 2 Diabetes, According to the Number of Risk Factors Within Target Range, With Structural Brain Abnormalities, Cognitive Performance, and Risk of Dementia

OBJECTIVE: Type 2 diabetes is associated with increased risks of cognitive dysfunction and brain abnormalities. The extent to which risk factor modification can mitigate these risks is unclear. We investigated the associations between incident dementia, cognitive performance, and brain abnormalities among individuals with type 2 diabetes, according to the number of risk factors on target, compared with control subjects without diabetes. RESEARCH DESIGN AND METHODS: Prospective data were from UK Biobank of 87,856 individuals (n = 10,663 diabetes, n = 77,193 control subjects; baseline 2006-2010), with dementia follow-up until February 2018. Individuals with diabetes were categorized according to the number of seven selected risk factors within the guideline-recommended target range (nonsmoking; guideline-recommended levels of glycated hemoglobin, blood pressure, BMI, albuminuria, physical activity, and diet). Outcomes were incident dementia, domain-specific cognitive performance, white matter hyperintensities, and total brain volume. RESULTS: After a mean follow-up of 9.0 years, 147 individuals (1.4%) with diabetes and 412 control subjects (0.5%) had incident dementia. Among individuals with diabetes, excess dementia risk decreased stepwise for a higher number of risk factors on target. Compared with control subjects (incidence rate per 1,000 person-years 0.62 [95% CI 0.56; 0.68]), individuals with diabetes who had five to seven risk factors on target had no significant excess dementia risk (absolute rate difference per 1,000 person-years 0.20 [-0.11; 0.52]; hazard ratio 1.32 [0.89; 1.95]). Similarly, differences in processing speed, executive function, and brain volumes were progressively smaller for a higher number of risk factors on target. These results were replicated in the Maastricht Study. CONCLUSIONS: Among individuals with diabetes, excess dementia risk, lower cognitive performance, and brain abnormalities decreased stepwise for a higher number of risk factors on target

Blood pressure variability and microvascular dysfunction:the Maastricht Study

Background: Microvascular dysfunction (MVD) contributes to stroke, dementia, depression, retinopathy and chronic kidney disease. However, the determinants of MVD are incompletely understood. Greater blood pressure variability (BPV) may be one such determinant. Methods and results: We used cross-sectional data of The Maastricht Study (n = 2773, age 59.9 years; 51.9% men) to investigate whether greater very short- to mid-term BPV is associated with various MVD measures. We standardized and averaged within-visit, 24-h and 7-day BPV into a systolic and a diastolic BPV composite score. MVD measures included a composite score of MRI cerebral small vessel disease (CSVD) features (total brain parenchymal volume, white matter hyperintensity volume, lacunar infarcts and cerebral microbleeds), a composite score of flicker light-induced retinal arteriolar and venular dilation response, albuminuria, heat-induced skin hyperemia and a composite score of plasma biomarkers of MVD (sICAM-1, sVCAM-1, sE-selectin and von Willebrand Factor). We used linear regression adjusted for age, sex, glucose metabolism status, mean 24-h systolic or DBP, cardiovascular risk factors and antihypertensive medication. We found that higher systolic and diastolic BPV composite scores (per SD) were associated with higher albuminuria [higher ratio, 1.04 (95% CI 1.00–1.08) and 1.07 (1.03–1.11), respectively], but not with other measures of MVD tested. Conclusion: Greater systolic and diastolic BPV was associated with higher albuminuria, but not with CSVD features, flicker light-induced retinal arteriolar and venular dilation response, heat-induced skin hyperemia and plasma biomarkers of MVD. This suggests that the microvasculature of the kidneys is most vulnerable to the detrimental effects of greater BPV

Effects of Pharmacogenetic Screening for CYP2D6 Among Elderly Starting Therapy With Nortriptyline or Venlafaxine:A Pragmatic Randomized Controlled Trial (CYSCE Trial)

PURPOSE/BACKGROUND: The duration of untreated depression is a predictor for poor future prognosis, making rapid dose finding essential. Genetic variation of the CYP2D6 isoenzyme can influence the optimal dosage needed for individual patients. The aim of this study was to determine the effectiveness of CYP2D6 pharmacogenetic screening to accelerate drug dosing in older patients with depression initiating nortriptyline or venlafaxine. METHODS/PROCEDURES: In this randomized controlled trial, patients were randomly allocated to one of the study arms. In the intervention arm (DG-I), the specific genotype accompanied by a standardized dosing recommendation based on the patients' genotype and the prescribed drug was directly communicated to the physician of the participant. In both the deviating genotype control arm (DG-C) and the nonrandomized control arm, the physician of the participants was not informed about the genotype and the associated dosing advise. The primary outcome was the time needed to reach adequate drug levels: (1) blood levels within the therapeutic range and (2) no dose adjustments within the previous 3 weeks. FINDINGS/RESULTS: No significant difference was observed in mean time to reach adequate dose or time to adequate dose between DG-I and DG-C. Compared with the nonrandomized control arm group, adequate drug levels were reached significantly faster in the DG-I group (log-rank test; P = 0.004), and there was a similar nonsignificant trend for the DG-C group (log-rank test; P = 0.087). IMPLICATIONS/CONCLUSIONS: The results of this study do not support pharmacogenetic CYP2D6 screening to accelerate dose adjustment for nortriptyline and venlafaxine in older patients with depression