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Memory (Hove, England) Jan 2023Inaccurate memory reports can have serious consequences within forensic and clinical settings, where emotion and misinformation are two common sources of memory... (Review)
Review
Inaccurate memory reports can have serious consequences within forensic and clinical settings, where emotion and misinformation are two common sources of memory distortion. Many studies have investigated how these factors are related; does emotion protect memory or leave it more vulnerable to the distorting effects of misinformation? The findings remain diffused. Thus, the present review aimed to clarify the relationship between emotion and susceptibility to misinformation. 39 eligible studies were reviewed. Results varied according to the type and dimension of emotion measured. Level of arousal may be unrelated to susceptibility to misinformation when retrieval occurs without delay; studies including delayed retrieval were limited. Stimuli valence may be associated with increased susceptibility to peripheral misinformation but unrelated to other misinformation. The following results were reported by limited studies: short-term distress and moderate levels of stress may decrease susceptibility, while anger and greater cortisol response to stress may increase susceptibility to misinformation. Source memory may also be unaffected by emotion. The results have important potential implications for forensic and clinical practice, for example by highlighting the value of enquiring witnesses' source memory. Methodological recommendations for future studies are made.
Topics: Humans; Emotions; Communication; Memory Disorders; Arousal; Health Status; Mental Recall
PubMed: 36093958
DOI: 10.1080/09658211.2022.2120623 -
Magnetic Resonance Imaging Dec 2023Multiple sclerosis (MS), namely the phenotype of the relapsing-remitting form, is the most common white matter disease and is mostly characterized by demyelination and... (Review)
Review
Multiple sclerosis (MS), namely the phenotype of the relapsing-remitting form, is the most common white matter disease and is mostly characterized by demyelination and inflammation, which lead to neurodegeneration and cognitive decline. Its diagnosis and monitoring are performed through conventional structural MRI, in which T2-hyperintense lesions can be identified, but this technique lacks sensitivity and specificity, mainly in detecting damage to normal appearing tissues. Models of diffusion-weighted MRI such as diffusion-tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) allow to uncover microstructural abnormalities that occur in MS, mainly in normal appearing tissues such as the normal appearing white matter (NAWM), which allows to overcome limitations of conventional MRI. DTI is the standard method used for modelling this kind of data, but it has limitations, which can be tackled by using more complex diffusion models, such as NODDI, which provides additional information on morphological properties of tissues. Although there are several studies in MS using both diffusion models, there is no formal assessment that summarizes the findings of both methods in lesioned and normal appearing tissues, and whether one is more advantageous than the other. Hence, this systematic review aims to identify what microstructural abnormalities are seen in lesions and/or NAWM in relapsing-remitting MS while using two different approaches to modelling diffusion data, namely DTI and NODDI, and if one of them is more appropriate than the other or if they are complementary to each other. The search was performed using PubMed, which was last searched on November 2022, and aimed at finding studies that either utilized both DTI and NODDI in the same dataset, or only one of the methods. Eleven articles were included in this review, which included cohorts with a relatively low sample size (total number of patients = 254, total number of healthy controls = 240), and patients with a moderate disease duration, all with relapsing-remitting MS. Overall, studies found decreased fractional anisotropy (FA), neurite density index (NDI) and orientation dispersion index (ODI), and increased mean, axial and radial diffusivities (MD, AD and RD, respectively) in lesions, when compared to contralateral NAWM and healthy controls' white matter. Compared to healthy controls' white matter, NAWM showed lower FA and NDI and higher MD, AD, RD, and ODI. Results from the included articles confirm that there is active demyelination and inflammation in both lesions and NAWM, as well as loss in neurites, and that structural damage is not confined to focal lesions, which is in concordance with histological findings and results from other imaging techniques. Furthermore, NODDI is suggested to have higher sensitivity and specificity, as seen by inspecting imaging results, compared to DTI, while still being clinically feasible. The use of biomarkers derived from such advanced diffusion models in clinical practice could imply a better understanding of treatment efficacy and disease progression, without relying on the manifestation of clinical symptoms, such as relapses.
Topics: Humans; Multiple Sclerosis; Diffusion Tensor Imaging; Diffusion Magnetic Resonance Imaging; White Matter; Brain; Neurites; Image Processing, Computer-Assisted
PubMed: 37775062
DOI: 10.1016/j.mri.2023.09.010 -
CNS Neuroscience & Therapeutics Feb 2024Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and... (Review)
Review
BACKGROUND AND OBJECTIVE
Amyotrophic lateral sclerosis (ALS) is a progressive motor and extra-motor neurodegenerative disease. This systematic review aimed to examine MRI biomarkers and neuropsychological assessments of the hippocampal and parahippocampal regions in patients with ALS.
METHODS
A systematic review was conducted in the Scopus and PubMed databases for studies published between January 2000 and July 2023. The inclusion criteria were (1) MRI studies to assess hippocampal and parahippocampal regions in ALS patients, and (2) studies reporting neuropsychological data in patients with ALS.
RESULTS
A total of 46 studies were included. Structural MRI revealed hippocampal atrophy, especially in ALS-FTD, involving specific subregions (CA1, dentate gyrus). Disease progression and genetic factors impacted atrophy patterns. Diffusion tensor imaging (DTI) showed increased mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and decreased fractional anisotropy (FA) in the hippocampal tracts and adjacent regions, indicating loss of neuronal and white matter integrity. Functional MRI (fMRI) revealed reduced functional connectivity (FC) between the hippocampus, parahippocampus, and other regions, suggesting disrupted networks. Perfusion MRI showed hypoperfusion in parahippocampal gyri. Magnetic resonance spectroscopy (MRS) found changes in the hippocampus, indicating neuronal loss. Neuropsychological tests showed associations between poorer memory and hippocampal atrophy or connectivity changes. CA1-2, dentate gyrus, and fimbria atrophy were correlated with worse memory.
CONCLUSIONS
The hippocampus and the connected regions are involved in ALS. Hippocampal atrophy disrupted connectivity and metabolite changes correlate with cognitive and functional decline. Specific subregions can be particularly affected. The hippocampus is a potential biomarker for disease monitoring and prognosis.
Topics: Humans; Diffusion Tensor Imaging; Amyotrophic Lateral Sclerosis; Neurodegenerative Diseases; Frontotemporal Dementia; Magnetic Resonance Imaging; Hippocampus; Biomarkers; Neuropsychological Tests; Atrophy
PubMed: 38334254
DOI: 10.1111/cns.14578 -
NeuroImage. Clinical 2020Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to... (Review)
Review
Diffusion magnetic resonance imaging (dMRI) is an imaging technique which probes the random motion of water molecules in tissues and has been widely applied to investigate changes in white matter microstructure in Alzheimer's Disease. This paper aims to systematically review studies that examined the effect of Alzheimer's risk genes on white matter microstructure. We assimilated findings from 37 studies and reviewed their diffusion pre-processing and analysis methods. Most studies estimate the diffusion tensor (DT) and compare derived quantitative measures such as fractional anisotropy and mean diffusivity between groups. Those with increased AD genetic risk are associated with reduced anisotropy and increased diffusivity across the brain, most notably the temporal and frontal lobes, cingulum and corpus callosum. Structural abnormalities are most evident amongst those with established Alzheimer's Disease. Recent studies employ signal representations and analysis frameworks beyond DT MRI but show that dMRI overall lacks specificity to disease pathology. However, as the field advances, these techniques may prove useful in pre-symptomatic diagnosis or staging of Alzheimer's disease.
Topics: Alzheimer Disease; Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; White Matter
PubMed: 32758801
DOI: 10.1016/j.nicl.2020.102359 -
Digital Health 2022Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature,... (Review)
Review
BACKGROUND
Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development.
PURPOSE
To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation.
METHODS
We conducted a systematic review of 572 brain tumour segmentation studies during 2015-2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score).
STATISTICAL TESTS
We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour.
RESULTS
We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation.
CONCLUSION
U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.
PubMed: 35340900
DOI: 10.1177/20552076221074122 -
European Journal of Radiology Jun 2023The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST... (Meta-Analysis)
Meta-Analysis Review
The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST criteria. However, therapy may alter tissue composition and not only tumor size, therefore, functional imaging techniques such as diffusion-weighted magnetic resonance imaging (DWI) may offer a more comprehensive assessment of treatment response. The aim of this systematic review and meta-analysis was to evaluate the use of DWI in the prediction and assessment of response to treatment in colorectal liver metastases and to determine if there is a baseline apparent diffusion coefficient (ADC) cut-off value that can predict a favorable response. A literature search was conducted using the MEDLINE/PubMed database, and risk of bias was evaluated using the QUADAS-2 tool. The mean differences between responders and non-responders were pooled. A total of 16 studies met the inclusion criteria, and various diffusion-derived techniques and coefficients were found to have potential for predicting and assessing treatment response. However, discrepancies were noted between studies. The most consistent predictor of response was a lower baseline ADC value calculated using traditional mono-exponential methods. Non-mono-exponential techniques for calculating DWI-derived parameters were also reported. A meta-analysis of a subset of studies failed to establish a cut-off value of ADC due to heterogeneity, but revealed a pooled mean difference of -0.12 × 10 mm/s between responders and non-responders. The results of this systematic review suggest that diffusion-derived techniques and coefficients may contribute to the evaluation and prediction of treatment response in colorectal liver metastases. Further controlled prospective studies are needed to confirm these findings and to guide clinical and radiological decision-making in the management of patients with CRC liver metastases.
Topics: Humans; Colorectal Neoplasms; Diffusion Magnetic Resonance Imaging; Liver Neoplasms; Embolization, Therapeutic; Prospective Studies; Treatment Outcome
PubMed: 37075628
DOI: 10.1016/j.ejrad.2023.110810 -
Neuroscience and Biobehavioral Reviews Dec 2022Major Depressive Disorder (MDD) and Bipolar Disorder Depression (BDD) are common psychiatric illnesses characterized by structural and functional brain alterations and... (Review)
Review
Major Depressive Disorder (MDD) and Bipolar Disorder Depression (BDD) are common psychiatric illnesses characterized by structural and functional brain alterations and signs of neuroinflammation. In line with the neuroinflammatory pathogenesis of depressive syndromes, recent studies have demonstrated how white matter (WM) microstructural impairments detected by Diffusion Tensor Imaging, are correlated to peripheral immunomarkers in depressed patients. In this context, we performed a comprehensive systematic search on PubMed, Medline and Scopus of the original studies published till June 2022, exploring the association between immunomarkers and WM alteration patterns in patients affected by MDD or BDD. Overall, the studies included in this review showed a consistent association between blood proinflammatory and counter-regulatory immunomarkers, including regulatory T cells and natural killer cells markers, as well as measures of demyelination and dysmyelination in both MDD and BDD patients. These pathogenetic insights could outline an integrated clinical perspective to affective disorders, helping psychiatrists to develop novel biotype-to-phenotype models of depression and opening the way to tailored approaches in treatments.
Topics: Humans; Bipolar Disorder; Depressive Disorder, Major; Diffusion Tensor Imaging; Inflammation; White Matter
PubMed: 36272579
DOI: 10.1016/j.neubiorev.2022.104922 -
Frontiers in Aging Neuroscience 2022To investigate the association between diffusion tensor imaging (DTI) findings and domain-specific cognitive impairment in cerebral small vessel disease (CSVD).
OBJECTIVE
To investigate the association between diffusion tensor imaging (DTI) findings and domain-specific cognitive impairment in cerebral small vessel disease (CSVD).
METHODS
Databases such as PubMed, Excerpta Medical Database (EMBASE), Web of Science, Cochrane Library, Chinese National Knowledge Infrastructure Databases (CNKI), Wanfang, Chinese Biomedical Literature Database (SinoMed), and Chongqing Chinese Science and Technology Periodical Database (VIP) were comprehensively retrieved for studies that reported correlation coefficients between cognition and DTI values. Random effects models and meta-regression were applied to account for heterogeneity among study results. Subgroup and publication bias analyses were performed using Stata software.
RESULTS
Seventy-seven studies involving 6,558 participants were included in our meta-analysis. The diagnosis classification included CSVD, white matter hyperintensities (WMH), subcortical ischemic vascular disease, cerebral microbleeding, cerebral amyloid angiopathy (CAA), cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), and Fabry disease. The pooled estimates showed that the fractional anisotropy (FA)-overall exhibited a moderate correlation with general cognition, executive function, attention, construction, and motor performance ( = 0.451, 0.339, 0.410, and 0.319), and the mean diffusitivity/apparent diffusion coefficient (MD/ADC)-overall was moderately associated with general cognition, executive function, and memory ( = -0.388, -0.332, and -0.303, respectively; < 0.05). Moreover, FA in cingulate gyrus (CG), cerebral peduncle (CP), corona radiata (CR), external capsule (EC), frontal lobe (FL), fornix (FOR), internal capsule (IC), and thalamic radiation (TR) was strongly correlated with general cognition ( = 0.591, 0.584, 0.543, 0.662, 0.614, 0.543, 0.597, and 0.571), and a strong correlation was found between MD/ADC and CG ( = -0.526), normal-appearing white matter (NAWM; = -0.546), and whole brain white matter (WBWM; = -0.505). FA in fronto-occipital fasciculus (FOF) ( = 0.523) and FL ( = 0.509) was strongly associated with executive function. Only MD/ADC of the corpus callosum (CC) was strongly associated with memory ( = -0.730). Besides, FA in CG ( = 0.532), CC ( = 0.538), and FL ( = 0.732) was strongly related to the attention domain. Finally, we found that the sample size, etiology, magnetic resonance imaging (MRI) magnet strength, study type, and study quality contributed to interstudy heterogeneity.
CONCLUSION
Lower FA or higher MD/ADC values were related to more severe cognitive impairment. General cognition and executive function domains attracted the greatest interest. The FL was commonly examined and strongly associated with general cognition, executive function, and attention. The CC was strongly associated with memory and attention. The CG was strongly related to general cognition and attention. The CR, IC, and TR were also strongly related to general cognition. Indeed, these results should be validated in high-quality prospective studies with larger sample sizes.
SYSTEMATIC REVIEW REGISTRATION
http://www.crd.york.ac.uk/PROSPERO, identifier: CRD42021226133.
PubMed: 36483114
DOI: 10.3389/fnagi.2022.1019088 -
Journal of Magnetic Resonance Imaging :... Oct 2023Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify... (Review)
Review
Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify subtle changes and early lesions in MS. Among these models, neurite orientation dispersion and density imaging (NODDI) is an emerging approach, quantifying specific neurite morphology in both grey (GM) and white matter (WM) tissue and increasing the specificity of diffusion imaging. In this systematic review, we summarized the NODDI findings in MS. A search was conducted on PubMed, Scopus, and Embase, which yielded a total number of 24 eligible studies. Compared to healthy tissue, these studies identified consistent alterations in NODDI metrics involving WM (neurite density index), and GM lesions (neurite density index), or normal-appearing WM tissue (isotropic volume fraction and neurite density index). Despite some limitations, we pointed out the potential of NODDI in MS to unravel microstructural alterations. These results might pave the way to a deeper understanding of the pathophysiological mechanism of MS. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
Topics: Humans; Neurites; Diffusion Tensor Imaging; Diffusion Magnetic Resonance Imaging; Multiple Sclerosis; White Matter; Brain
PubMed: 37042392
DOI: 10.1002/jmri.28727 -
Predicting Survival in Glioblastoma Patients Using Diffusion MR Imaging Metrics-A Systematic Review.Cancers Oct 2020Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer... (Review)
Review
Despite advances in surgical and medical treatment of glioblastoma (GBM), the medium survival is about 15 months and varies significantly, with occasional longer survivors and individuals whose tumours show a significant response to therapy with respect to others. Diffusion MRI can provide a quantitative assessment of the intratumoral heterogeneity of GBM infiltration, which is of clinical significance for targeted surgery and therapy, and aimed at improving GBM patient survival. So, the aim of this systematic review is to assess the role of diffusion MRI metrics in predicting survival of patients with GBM. According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a systematic literature search was performed to identify original articles since 2010 that evaluated the association of diffusion MRI metrics with overall survival (OS) and progression-free survival (PFS). The quality of the included studies was evaluated using the QUIPS tool. A total of 52 articles were selected. The most examined metrics were associated with the standard Diffusion Weighted Imaging (DWI) (34 studies) and Diffusion Tensor Imaging (DTI) models (17 studies). Our findings showed that quantitative diffusion MRI metrics provide useful information for predicting survival outcomes in GBM patients, mainly in combination with other clinical and multimodality imaging parameters.
PubMed: 33020420
DOI: 10.3390/cancers12102858