Towards closed-loop precision psychiatry: Integrating MRI biomarkers for individualized care of major depressive disorder

BACKGROUND Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. OBJECTIVE We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. METHODS The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T<sub>1</sub>-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. RESULTS The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and <i>F</i><sub>1</sub>-score (93.3%). CONCLUSIONS The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

BackgroundEarly detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.ObjectiveWe aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.MethodsThe study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.ResultsThe support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).ConclusionsThe results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

CSF TOTAL TAU AS A BIOMARKER FOR NEURONAL INJURY IN ALZHEIMER’S DISEASE: ALIGNING RATES OF CSF CHANGE WITH RATES OF HIPPOCAMPAL AND CORTICAL GRAY MATTER ATROPHY

Background.We sought to determine the mediating effects of magnetic resonance imaging (MRI) biomarkers at term gestation on the relationship between perinatal illness-severity and neurodevelopment.Methods.The Clinical Risk Index for Babies – second edition (CRIB-II) was correlated with indices of brain maturation or injury and neurodevelopment at 2-year follow-up in infants born less than 32 weeks gestation. Using a counterfactual mediation analysis, associations between CRIB-II, MRI biomarkers and neurodevelopment were confirmed, followed by an assessment of the mediating effects of MRI biomarkers on the relationship between CRIB-II and neurodevelopment.Results:CRIB-II correlated significantly with neurodevelopment and MRI biomarkers of brain injury or cortical maturation. Two MRI biomarkers, cortical surface area and global injury score, were associated with neurodevelopmental scores at follow-up and included in mediation analyses.Conclusion:Biomarkers of cortical maturation or brain injury at term equivalent age mediated a substantial portion of the risks conveyed by perinatal illness-severity on neurodevelopmental outcomes at 2 years corrected age.

IntroductionThis study aims to determine whether newly introduced biomarkers Visinin‐like protein‐1 (VILIP‐1), chitinase‐3‐like protein 1 (YKL‐40), synaptosomal‐associated protein 25 (SNAP‐25), and neurogranin (NG) in cerebrospinal fluid are useful in evaluating the asymptomatic and early symptomatic stages of Alzheimer's disease (AD). It further aims to shed new insight into the differences between stable subjects and those who progress to AD by associating cerebrospinal fluid (CSF) biomarkers and specific magnetic resonance imaging (MRI) regions with disease progression, more deeply exploring how such biomarkers relate to AD pathology.MethodsWe examined baseline and longitudinal changes over a 7‐year span and the longitudinal interactions between CSF and MRI biomarkers for subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We stratified all CSF (140) and MRI (525) cohort participants into five diagnostic groups (including converters) further dichotomized by CSF amyloid beta (Aβ) status. Linear mixed models were used to compare within‐person rates of change across diagnostic groups and to evaluate the association of CSF biomarkers as predictors of magnetic resonance imaging (MRI) biomarkers. CSF biomarkers and disease‐prone MRI regions are assessed for CSF proteins levels and brain structural changes.ResultsVILIP‐1 and SNAP‐25 displayed within‐person increments in early symptomatic, amyloid‐positive groups. CSF amyloid‐positive (Aβ+) subjects showed elevated baseline levels of total tau (tTau), phospho‐tau181 (pTau), VILIP‐1, and NG. YKL‐40, SNAP‐25, and NG are positively intercorrelated. Aβ+ subjects showed negative MRI biomarker changes. YKL‐40, tTau, pTau, and VILIP‐1 are longitudinally associated with MRI biomarkers atrophy.DiscussionConverters (CNc, MCIc) highlight the evolution of biomarkers during the disease progression. Results show that underlying amyloid pathology is associated with accelerated cognitive impairment. CSF levels of Aβ42, pTau, tTau, VILIP‐1, and SNAP‐25 show utility to discriminate between mild cognitive impairment (MCI) converter and control subjects (CN). Higher levels of YKL‐40 in the Aβ+ group were longitudinally associated with declines in temporal pole and entorhinal thickness. Increased levels of tTau, pTau, and VILIP‐1 in the Aβ+ groups were longitudinally associated with declines in hippocampal volume. These CSF biomarkers should be used in assessing the characterization of the AD progression.

Battle against Alzheimer's Disease: The Scope and Potential Value of Magnetic Resonance Imaging Biomarkers

BackgroundProstate cancer (PCa) is the most diagnosed cancer in men, with an increasing need to integrate noninvasive imaging and circulating microRNAs beyond prostate-specific antigen for screening and early detection. ObjectiveTo validate magnetic resonance imaging (MRI) biomarkers and circulating microRNAs as triage tests for patients directed to prostate biopsy, and to test different diagnostic pathways to compare their performance on patients’ outcome, in terms of unnecessary biopsy avoidance. Design, setting, and participantsA prospective single-center cohort study, enrolling patients with PCa suspicion who underwent MRI, MRI-directed fusion biopsy (MRDB), and circulating microRNAs, was conducted. A network-based analysis was used to identify MRI biomarkers and microRNA drivers of clinically significant PCa. InterventionMRI, MRDB, and blood sampling. Outcome measurements and statistical analysisThe decision curve analysis was exploited to assess the performance of the proposed diagnostic pathways and to quantify their benefit in terms of biopsy avoidance. Results and limitationsOverall, 261 men were enrolled and underwent MRDB for PCa detection. A total of 178 patients represented the entire cohort: 55 (30.9%) were negative for PCa, 39 (21.9%) had grade group (GG) 1 PCa, and 84 (47.2%) had GG >1 PCa. The proposed integrated pathway, including clinical data, MRI biomarkers, and microRNAs, provided the best net benefit with a biopsy avoidance rate of about 20% at a low disease probability. The main limitation is the monocentric design in a referral center. ConclusionsThe integrated pathway represents a validated model that sees MRI biomarkers and microRNAs as a prebiopsy triage of patients at a risk for clinically significant PCa. The proposed pathway showed the highest net benefit in terms of unnecessary biopsy avoidance. Patient summaryThe proposed integrated pathway for early detection of prostate cancer (PCa) allows accurate patient allocation to biopsy and patients’ stratification into risk group categories, reducing overdiagnosis and overtreatment of clinically insignificant PCa.

Introduction Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated with cognitive and behavioral impairments and motor symptoms. Magnetic resonance imaging (MRI) biomarkers have been investigated as potential tools for detecting and monitoring memory-related impairment in ALS. Our objective was to examine the importance of identifying MRI biomarkers for memory-related impairment in ALS, motor neuron disease (MND), and ALS frontotemporal dementia (FTD) (ALS-FTD) patients. Methods PubMed and Scopus databases were searched. Keywords covering magnetic resonance imaging, ALS, MND, and memory impairments were searched. There were a total of 25 studies included in our work here. Results The structural MRI (sMRI) studies reported gray matter (GM) atrophy in the regions associated with memory processing, such as the hippocampus and parahippocampal gyrus (PhG), in ALS patients. The diffusion tensor imaging (DTI) studies showed white matter (WM) alterations in the corticospinal tract (CST) and other tracts that are related to motor and extra-motor functions, and these alterations were associated with memory and executive function impairments in ALS. The functional MRI (fMRI) studies also demonstrated an altered activation in the prefrontal cortex, limbic system, and other brain regions involved in memory and emotional processing in ALS patients. Conclusion MRI biomarkers show promise in uncovering the neural mechanisms of memory-related impairment in ALS. Nonetheless, addressing challenges such as sample sizes, imaging protocols, and longitudinal studies is crucial for future research. Ultimately, MRI biomarkers have the potential to be a tool for detecting and monitoring memory-related impairments in ALS.

Pre-clinical animal models are important to study the fundamental biological and functional mechanisms involved in the longitudinal evolution of heart failure (HF). Particularly, large animal models, like nonhuman primates (NHPs), that possess greater physiological, biochemical, and phylogenetic similarity to humans are gaining interest. To assess the translatability of these models into human diseases, imaging biomarkers play a significant role in non-invasive phenotyping, prediction of downstream remodeling, and evaluation of novel experimental therapeutics. This paper sheds insight into NHP cardiac function through the quantification of magnetic resonance (MR) imaging biomarkers that comprehensively characterize the spatiotemporal dynamics of left ventricular (LV) systolic pumping and LV diastolic relaxation. MR tagging and phase contrast (PC) imaging were used to quantify NHP cardiac strain and flow. Temporal inter-relationships between rotational mechanics, myocardial strain and LV chamber flow are presented, and functional biomarkers are evaluated through test-retest repeatability and inter subject variability analyses. The temporal trends observed in strain and flow was similar to published data in humans. Our results indicate a dominant dimension based pumping during early systole, followed by a torsion dominant pumping action during late systole. Early diastole is characterized by close to 65% of untwist, the remainder of which likely contributes to efficient filling during atrial kick. Our data reveal that moderate to good intra-subject repeatability was observed for peak strain, strain-rates, E/circumferential strain-rate (CSR) ratio, E/longitudinal strain-rate (LSR) ratio, and deceleration time. The inter-subject variability was high for strain dyssynchrony, diastolic strain-rates, peak torsion and peak untwist rate. We have successfully characterized cardiac function in NHPs using MR imaging. Peak strain, average systolic strain-rate, diastolic E/CSR and E/LSR ratios, and deceleration time were identified as robust biomarkers that could potentially be applied to future pre-clinical drug studies.

Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy

BackgroundAt present, the simple prognostic models based on clinical information for predicting the treatment outcomes of brain metastases (BMs) are subjective and delayed. Thus, we performed this systematic review of multiple studies to assess the potential of quantitative magnetic resonance imaging (MRI) biomarkers for the early prediction of treatment outcomes of brain metastases with stereotactic radiosurgery (SRS).MethodsWe systematically searched the PubMed, Embase, Cochrane, Web of Science, and Clinical Trials.gov databases for articles published between February 1, 1991, and April 11, 2022, with no language restrictions. We included studies involving patients with BMs receiving SRS; the included patients were required to have definite pathology of a primary tumor and complete imaging data (pre- and post-SRS). We excluded the articles that included patients who had undergone previous surgery and those that did not include regular follow-up or corresponding MRI scans.ResultsWe identified 2,162 studies, of which 26 were included in our analysis, involving a total of 1,362 participants. All 26 studies explored the relevant MRI parameters to predict the prognosis of patients with BMs who received SRS. The outcomes were generalized according to the relationships between the anatomical/morphological, microstructural, vascular, and metabolic changes and SRS. Generally, with traditional MRI, there are several quantitative prognostic models based on preradiosurgical radiomics that predict the outcome of SRS treatment in local BM control. With the implementation of advanced MRI, the relative apparent diffusion coefficient (ADC), perfusion fraction (f), relative cerebral blood volume (rCBV), relative regional cerebral blood flow (rrCBF), interstitial fluid pressure (IFP), quadratic of time-dependent leakage (Ktrans2), extracellular extravascular volume (ve), choline/creatine (Cho/Cr), nuclear Overhauser effect (NOE) peak, and intraextracellular water exchange rate constant (kIE) were confirmed to be indicative of the therapeutic effect of SRS for BMs.ConclusionsQuantitative MRI biomarkers extracted from traditional or advanced MRI at different time points, which can represent the anatomical/morphological, microstructural, vascular, and metabolic changes, respectively, have been proposed as promising markers for the early prediction of SRS response in those with BMs. There are some limitations in this review, including the risk of selection bias, the limited number of study objects, the incomparability of the total data, and the subjectivity of the review process.

PurposeTo evaluate whether the combination of imaging biomarkers obtained by means of different 3 Tesla (3T) Magnetic Resonance Imaging (MRI) advanced techniques can improve the diagnostic accuracy in the differentiation between benign and malignant single ring-enhancing brain masses.Materials and Methods14 patients presenting at conventional 3T MRI single brain mass with similar appearance as regard ring enhancement, presence of peri-lesional edema and absence of hemorrhage signs were included in the study. All lesions were histologically proven: 5 pyogenic abscesses, 6 glioblastomas, and 3 metastases. MRI was performed at 3 Tesla and included Diffusion Weighted Imaging (DWI), Dynamic Susceptibility Contrast -Perfusion Weighted Imaging (DSC-PWI), Magnetic Resonance Spectroscopy (MRS), and Diffusion Tensor Imaging (DTI). Imaging biomarkers derived by those advanced techniques [Cerebral Blood Flow (CBF), relative Cerebral Blood Volume (rCBV), relative Main Transit Time (rMTT), Choline (Cho), Creatine (Cr), Succinate, N-Acetyl Aspartate (NAA), Lactate (Lac), Lipids, relative Apparent Diffusion Coefficient (rADC), and Fractional Anisotropy (FA)] were detected by two experienced neuroradiologists in joint session in 4 areas: Internal Cavity (IC), Ring Enhancement (RE), Peri-Lesional edema (PL), and Contralateral Normal Appearing White Matter (CNAWM). Significant differences between benign (n = 5) and malignant (n = 9) ring enhancing lesions were tested with Mann-Withney U test. The diagnostic accuracy of MRI biomarkers taken alone and MRI biomarkers ratios were tested with Receiver Operating Characteristic (ROC) analysis with an Area Under the Curve (AUC) ≥ 0.9 indicating a very good diagnostic accuracy of the variable.ResultsFive MRI biomarker ratios achieved excellent accuracy: IC-rADC/PL-NAA (AUC = 1), IC-rADC/IC-FA (AUC = 0.978), RE-rCBV/RE-FA (AUC = 0.933), IC-rADC/RE-FA (AUC = 0.911), and IC-rADC/PL-FA (AUC = 0.911). Only IC-rADC achieved a very good diagnostic accuracy (AUC = 0.909) among MRI biomarkers taken alone.ConclusionAlthough the major limitation of the study was the small sample size, preliminary results seem to suggest that combination of multiple 3T MRI biomarkers is a feasible approach to MRI biomarkers in order to improve diagnostic accuracy in the differentiation between benign and malignant single ring enhancing brain masses. Further studies in larger cohorts are needed to reach definitive conclusions.

Magnetic resonance imaging biomarkers indicate a central venous hypertension syndrome in patients with symptomatic pineal cysts

There is growing interest in developing magnetic resonance imaging (MRI) biomarkers of brain connectivity from resting-state functional (rs-fMRI) and diffusion tensor imaging (DTI) to aid in the diagnosis and management of patients with mild traumatic brain injury (mTBI). To determine whether early MRI biomarkers of brain connectivity are useful in predicting outcome after mTBI, we conducted a systematic review using the following inclusion criteria: 1) patients aged >16 years with mTBI, 2) MRI performed during the first month post-injury, 3) outcome measure available, 4) control group, and 5) original article published in a peer-reviewed journal. Of the 1351 citations identified, 14 studies met inclusion criteria (5 rs-fMRI and 10 DTI; 680 patients with mTBI vs. 436 controls) including those where MRI was performed from <12 h to 1 month post-injury. The most common clinical outcome measure used in these studies was symptom burden using the Rivermead Post-Concussion Questionnaire. The most frequently studied brain connectivity MRI biomarkers were global functional connectivity, default-mode network, and fractional anisotropy (FA). Despite the scant evidence and considerable methodological heterogeneity observed among studies, we conclude that brain connectivity MRI biomarkers obtained within 1 month of injury may be potentially useful in predicting outcome in mTBI. Further longitudinal studies are needed to evaluate the effect of mTBI on MRI-based brain connectivity biomarkers and examine how incorporation of these tests can inform the clinical care of individual mTBI patients.

The need to improve diagnostic precision in multiple sclerosis (MS) is widely recognized. In recent years, several novel magnetic resonance imaging (MRI) biomarkers have been proposed to enhance diagnostic specificity and reduce misdiagnosis. Some of these imaging biomarkers are deemed highly specific for MS and are likely ready to enter the MS diagnostic work-up, while others are still in their exploratory phase. In addition, new synthetic MRI contrasts and artificial intelligence-based diagnostic algorithms are being tested to reduce the time burden related to imaging data acquisition and analysis. In this review, we summarize the most recent advancement in the field, focusing on the adoption of these novel MRI biomarkers—whether used alone or in combination—for the differential diagnosis of MS.