Magnetic resonance imaging and spectroscopy in neonatal encephalopathy: current consensus position and future opportunities.

Alzheimer’s disease (AD) is the most common progressive neurodegenerative disorder characterized by gradual deterioration in cognition, function, and behavior. The number of patients with dementia is increasing in the Western world. Neuroimaging plays an important role in the study and clinical diagnosis of AD. It is of utmost importance to exclude potentially treatable conditions such as brain tumors, hematomas, and hydrocephalus. According to a meta-analysis potentially reversible causes were seen in 9% and actually reversed in only 0.6% of dementia cases (0.29% partially, 0.31% fully) (1). Techniques that are widely used in clinical practice are still largely based on structural magnetic resonance (MR) imaging and volumetry. Recently Wattjes et al. (2) concluded that although MR imaging should be the preferred imaging modality due its lack of ionizing radiation and higher contrast resolution, 64-detector row computed tomography (CT) is a suitable and accurate imaging method with which to evaluate global cortical atrophy, medial temporal lobe atrophy, and white matter changes in a memory clinic setting, and it can be considered a nearly equivalent alternative to MR imaging in patients who cannot undergo MR examination. However, it should be noted that measures of brain atrophy typically reflect the relatively late stages of neuronal loss and may not be ideal for identifying early neuropathological changes. There is increasing evidence showing that the pathological process associated with AD may begin years or decades before diagnosis. But: do the advanced techniques help in assessing therapeutic effects or improve patient outcome? Early detection of AD risk would enable preventive or more effective treatment of the patients, resulting in a time delay in symptom onset that may decrease the prevalence of the disease (3). Therefore, in addition to the structural MR imaging procedures a spectrum of novel MR imaging tools is being adopted for the scientific study of pathological processes underlying the development of AD, including blood oxygenation level-dependent (BOLD) functional MRI studies of brain activation and functional connectivity, perfusion measurements with arterial spin labeling (ASL), MR spectroscopy (MRS), and diffusion tensor imaging (DTI). Functional MRI on BOLD contrast allows noninvasive in vivo assessment of hemodynamic responses to external stimuli. ASL is a non-invasive MR imaging technique for the measurement of regional cerebral blood flow by labeling of the arterial water and using it as an endogenous tracer. Although challenging (4), hippocampal MRS combined with quantitative measurements of hippocampal atrophy may improve the early diagnosis of AD. DTI can be used to determine the orientation of fiber tracts and the neural network connections between different brain regions. Fractional anisotropy has become an imaging marker commonly used to study microstructural white matter abnormalities in various pathological states. There is growing interest in using DTI for AD studies (5, 6). In this issue of Acta Radiologica a comprehensive state of the art review by Dr Li Tie-Qiang (3) discusses the use of these advanced imaging techniques mainly in research. The review allows you to get an excellent overview of the advanced MR imaging techniques and the article is highly recommended reading.

High-Resolution fMRI Reveals Laminar Differences in Neurovascular Coupling between Positive and Negative BOLD Responses

Apport pronostique de la résonance magnétique cérébrale dans l’encéphalopathie hypoxique-ischémique du nouveau-né à terme : score d’imagerie, spectroscopie. Étude de 26 cas

Recent MRI advances in experimental stroke.

To construct and undertake preliminary validation of a magnetic resonance (MR) imaging scoring system designed for use in pelvic MR imaging performed for characterization of adnexal masses that were indeterminate at ultrasonography (US). The institutional ethics committee approved this retrospective study and granted a waiver of informed consent. The study population comprised 394 women who underwent MR imaging between January 1, 2008, and October 30, 2010, for characterization of 497 adnexal masses that were seen at US. Then, masses were chronologically divided into a training set (329 masses) and a validating set (168 masses). Two radiologists who were blinded to the clinical findings retrospectively evaluated MR imaging criteria for characterization of adnexal masses. In the training set, the positive likelihood ratio (PLR) of malignancy and κ values were calculated for each criterion. The reference standard was surgical pathologic findings or findings at imaging follow-up of at least 1 year. On the basis of the PLR and multivariate analysis, a five-category MR scoring system called the ADNEX MR SCORING system was created and was subsequently tested by six readers with the validating set. There was almost perfect agreement (κ > 0.80) for each MR imaging feature except for grouped septa (κ = 0.558) and thickened regular septa (κ = 0.555). The classification was accurate in both the training set (area under the receiver operating characteristic [ROC] curve [AUC] = 0.981 for reader 1 and 0.961 for reader 2) and the validating set (AUC = 0.964 for reader 1 and 0.943 for reader 2). ROC curve analysis demonstrated that the optimal cutoff point was an ADNEX MR score of 3; an ADNEX MR score of 4 or higher was associated with malignancy with a sensitivity of 93.5% (58 of 62) and a specificity of 96.6% (258 of 267). In this study, a reproducible and accurate MR imaging scoring system that has the potential to improve patient care was developed and tested. Multicenter prospective validation of the score is warranted. http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.13121161/-/DC1.

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.

Background and aims: Deep grey matter lactate/N-acetylaspartate (Lac/NAA) peak area ratio on proton magnetic resonance spectroscopy (1H MRS) is the best predictor of neurological outcome following neonatal encephalopathy (NE). We evaluated the prognostic accuracy following therapeutic hypothermia (TH).Methods: We performed magnetic resonance imaging (MRI) in consecutive term babies with NE, over a 3 year period before and after introduction of TH. Death or severe neurodisability (BSID 3) at 12 months was considered as an unfavourable outcome.Results: Of 28 babies studied, 11 babies received TH. The median (range) age at MRI was 10 (5,12) days. Thalamic Lac/NAA (>0.29) had 100% sensitivity and specificity for predicting adverse outcome in both normothermic babies and cooled infants (Table 1). The mean (SD) Lac/NAA was high in infants who had unfavorable outcome (Figure 1)Table Full size tableConclusions: The prognostic accuracy of 1H MRS deep gray matter Lac/NAA peak area ratios is unaltered by TH.

Measurement of cerebrovascular reactivity (CVR) can give valuable information about existing pathology and the risk of adverse events, such as stroke. A common method of obtaining regional CVR values is by measuring the blood flow response to carbon dioxide (CO2)-enriched air using arterial spin labeling (ASL) or blood oxygen level-dependent (BOLD) imaging. Recently, several studies have used carbogen gas (containing only CO2 and oxygen) as an alternative stimulus. A direct comparison was performed between CVR values acquired by ASL and BOLD imaging using stimuli of (1) 5% CO2 in air and (2) 5% CO2 in oxygen (carbogen-5). Although BOLD and ASL CVR values are shown to be correlated for CO2 in air (mean response 0.11±0.03% BOLD, 4.46±1.80% ASL, n=16 hemispheres), this correlation disappears during a carbogen stimulus (0.36±0.06% BOLD, 4.97±1.30% ASL). It is concluded that BOLD imaging should generally not be used in conjunction with a carbogen stimulus when measuring CVR, and that care must be taken when interpreting CVR as measured by ASL, as values obtained from different stimuli (CO2 in air versus carbogen) are not directly comparable.

Our aim was to assess brain myo-inositol/creatine plus phosphocreatine (Cr) in the first week in term infants with neonatal encephalopathy using localized short echo time proton magnetic resonance spectroscopy and to relate this to measures of brain injury, specifically lactate/Cr in the first week, basal ganglia changes on magnetic resonance imaging (MRI), and neurodevelopmental outcome at 1 y. Fourteen term infants with neonatal encephalopathy of gestational age (mean +/- SD) 39.6 +/- 1.6 wk, birth weight 3270 +/- 490 g, underwent MRI and magnetic resonance spectroscopy at 3.5 +/- 2.1 d. Five infants were entered in a pilot study of treatment with moderate whole-body hypothermia for neonatal encephalopathy; two were being cooled at the time of the scan. T(1)- and T(2)-weighted transverse magnetic resonance images were graded as normal or abnormal according to the presence or absence of the normal signal intensity of the posterior limb of the internal capsule and signal intensity changes in the basal ganglia. Localized proton magnetic resonance spectroscopy data were obtained from an 8-cm(3) voxel in the basal ganglia using echo times of 40 and 270 ms, and the peak area ratios of myo-inositol/Cr and lactate/Cr were measured. Outcome was scored using Griffith's development scales and neurodevelopmental examination at 1 y. MRI and outcome were normal in six infants and abnormal in eight. myo-Inositol/Cr and lactate/Cr were higher in infants with abnormal MRI and outcome (p < 0.01, p < 0.01, respectively). myo-Inositol/Cr and lactate/Cr were correlated (p < 0.01) and were both correlated to the Griffith's developmental scales (p < 0.01, p < 0.01, respectively). In conclusion, these preliminary data suggest that early increases in brain basal ganglia myo-inositol/Cr in infants with neonatal encephalopathy are associated with increased lactate/Cr, MRI changes of severe injury, and a poor neurodevelopmental outcome at 1 y.

Functional magnetic resonance imaging (fMRI) is a unique window to the brain, enabling scientists to follow changes in brain activity in response to hormones, ageing, environment, drugs of abuse and other stimuli. There are two features that make fMRI unique when compared with other imaging modalities used in behavioural neuroscience. First, it can be entirely noninvasive: each animal can serve as its own control over the natural course of its life, vital for following neuroadaptation and other developmental processes critical to understanding behaviour. Second, fMRI has the spatial and temporal resolution to observe patterns of neuronal activity across the entire brain in less than a minute. Although fMRI does not have the cellular spatial resolution of immunostaining, nor the millisecond temporal resolution of electrophysiology, synchronised changes in neuronal activity across multiple brain areas seen with functional MRI can be viewed as functional neuroanatomical circuits coordinating the thoughts, memories and emotions for particular behaviours. Thus, fMRI affords a systems approach to the study of the brain, complementing and building from other neurobiological techniques to understand how behaviour is organised across multiple brain regions. In this review, we present a general background to fMRI and the different imaging modalities that can be used in fMRI studies. Included are examples of the application of fMRI in behavioural neuroscience research, along with discussion of the advantages and disadvantages of this technology.

BackgroundThe neurological condition known as multiple sclerosis (MS) is crippling and has a complicated pathogenesis as well as a wide range of clinical symptoms, including fatigue, difficulty walking, numbness or tingling, muscle spasms and spasticity, weakness, vision problems, dizziness and vertigo, bladder and bowel dysfunction, cognitive impairment, and emotional changes. The complete scope of MS pathology cannot be fully captured by conventional magnetic resonance imaging (MRI) sequences, which has led to the investigation of sophisticated MRI methods for better diagnosis and treatment.ObjectiveThis study aims to evaluate the clinical relevance of advanced MRI sequences in multiple sclerosis.MethodologyA retrospective cohort study was conducted across multiple specialized medical centers renowned for treating neurological disorders, particularly multiple sclerosis, and involved 310 patients with diverse geography seeking treatment throughout 2022. Records were searched to obtain patient information, demographics, and treatment history. Descriptive statistics and t-tests were among the statistical studies that investigated relationships between MRI biomarkers and clinical factors to help with the diagnosis and treatment of MS. A p-value of <0.05 was significant.ResultsThe research group consisted of 310 MS patients, the majority of whom were female (67.42%) and had a mean age of 34.7 years. With hypertension (14.52%) and hyperlipidemia (19.35%) as prevalent comorbidities, the majority of patients (72.26%) were on disease-modifying treatments. The results of advanced MRI showed that lesions with white matter had higher mean diffusivity (1.25 ± 0.15 mm²/s) on DWI, lesions with reduced magnetization transfer ratio (MTR) (0.15 ± 0.03) on MTI, and lesions with reduced fractional anisotropy (FA) (0.40 ± 0.08) on diffusion tensor imaging (DTI). Additionally, the blood oxygen level-dependent (BOLD) signals in cognitive processing regions (0.75 ± 0.10) on functional MRI were different from those with normal-appearing white matter (0.40 ± 0.08).ConclusionAdvanced MRI sequences are essential for bettering MS diagnosis, prognosis, and treatment because they link imaging biomarkers to important clinical parameters, which improves patient care and quality of life.

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.

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&lt;sub&gt;1&lt;/sub&gt;-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 &lt;i&gt;F&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt;-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.

BackgroundCurrent non-invasive approaches to monitoring disease activity in patients with lupus nephritis (LN) include serial measurement of glomerular filtration rate(GFR) and assessment of proteinuria. Both are influenced by renal damage...

While abnormalities related to concussion are typically not identified on traditional clinical neuroimaging (i.e., computed tomography [CT] or magnetic resonance imaging [MRI]), more sophisticated neuroimaging techniques have the potential to reveal the complex neurometabolic processes related to concussion and its recovery. Clinically, these techniques may one day provide useful information to guide clinicians in the management and treatment of sports concussion. This article critically reviews the current state of the literature regarding neuroimaging and sports concussion, identifies challenges in the application of these techniques, and identifies areas for future research.