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The ability to read minds has long been a fascination of science fiction, but revolutionary new brain-imaging methods are bringing it closer to scientific reality. The New Mind Readers provides a compelling look at the origins, development, and future of these extraordinary tools, revealing how they are increasingly being used to decode our thoughts and experiences--and how this raises sometimes troubling questions about their application in domains such as marketing, politics, and the law. Russell Poldrack takes readers on a journey of scientific discovery, telling the stories of the visionaries behind these breakthroughs. Along the way, he gives an insider's perspective on what is perhaps the single most important technology in cognitive neuroscience today--functional magnetic resonance imaging, or fMRI, which is providing astonishing new insights into the contents and workings of the mind. He highlights both the amazing power and major limitations of these techniques and describes how applications outside the lab often exceed the bounds of responsible science. Poldrack also details the unique and sometimes disorienting experience of having his own brain scanned more than a hundred times as part of a landmark study of how human brain function changes over time. Written by one of the world's leading pioneers in the field, The New Mind Readers cuts through the hype and misperceptions surrounding these emerging new methods, offering needed perspective on what they can and cannot do--and demonstrating how they can provide new answers to age-old questions about the nature of consciousness and what it means to be human. -- Inside jacket flap.

The amygdala and the hippocampus are two limbic structures that play a critical role in cognition and behavior, however their manual segmentation and that of their smaller nuclei/subfields in multicenter datasets is time consuming and difficult due to the low contrast of standard MRI. Here, we assessed the reliability of the automated segmentation of amygdalar nuclei and hippocampal subfields across sites and vendors using FreeSurfer in two independent cohorts of older and younger healthy adults. Sixty-five healthy older (cohort 1) and 68 younger subjects (cohort 2), from the PharmaCog and CoRR consortia, underwent repeated 3D-T1 MRI (interval 1–90 days). Segmentation was performed using FreeSurfer v6.0. Reliability was assessed using volume reproducibility error (ε) and spatial overlapping coefficient (DICE) between test and retest session. Significant MRI site and vendor effects (p ​< ​.05) were found in a few subfields/nuclei for the ε, while extensive effects were found for the DICE score of most subfields/nuclei. Reliability was strongly influenced by volume, as ε correlated negatively and DICE correlated positively with volume size of structures (absolute value of Spearman’s r correlations >0.43, p ​< ​1.39E-36). In particular, volumes larger than 200 ​mm3 (for amygdalar nuclei) and 300 ​mm3 (for hippocampal subfields, except for molecular layer) had the best test-retest reproducibility (ε ​< ​5% and DICE ​> ​0.80). Our results support the use of volumetric measures of larger amygdalar nuclei and hippocampal subfields in multisite MRI studies. These measures could be useful for disease tracking and assessment of efficacy in drug trials. •Differences in MRI site/vendor had a limited effect on volume reproducibility.•Differences in MRI site/vendor had an extensive effect on spatial accuracy.•Reliability is good for larger amygdalar and hippocampal structures.•Automated volumetry is reliable in multicenter MRI studies.

BackgroundBreast cancer remains a leading cause of death in women and novel imaging biomarkers are urgently required. Here, we demonstrate the diagnostic and treatment-monitoring potential of non-invasive sodium (23Na) MRI in preclinical models of breast cancer.MethodsFemale Rag2−/−Il2rg−/− and Balb/c mice bearing orthotopic breast tumours (MDA-MB-231, EMT6 and 4T1) underwent MRI as part of a randomised, controlled, interventional study. Tumour biology was probed using ex vivo fluorescence microscopy and electrophysiology.Results23Na MRI revealed elevated sodium concentration ([Na+]) in tumours vs non-tumour regions. Complementary proton-based diffusion-weighted imaging (DWI) linked elevated tumour [Na+] to increased cellularity. Combining 23Na MRI and DWI measurements enabled superior classification accuracy of tumour vs non-tumour regions compared with either parameter alone. Ex vivo assessment of isolated tumour slices confirmed elevated intracellular [Na+] ([Na+]i); extracellular [Na+] ([Na+]e) remained unchanged. Treatment with specific inward Na+ conductance inhibitors (cariporide, eslicarbazepine acetate) did not affect tumour [Na+]. Nonetheless, effective treatment with docetaxel reduced tumour [Na+], whereas DWI measures were unchanged.ConclusionsOrthotopic breast cancer models exhibit elevated tumour [Na+] that is driven by aberrantly elevated [Na+]i. Moreover, 23Na MRI enhances the diagnostic capability of DWI and represents a novel, non-invasive biomarker of treatment response with superior sensitivity compared to DWI alone.

Magnetic Resonance Imaging (MRI) is among the most important medical imaging techniques available today. There is an installed base of approximately 15,000 MRI scanners worldwide. Each of these scanners is capable of running many different \"pulse sequences\", which are governed by physics and engineering principles, and implemented by software programs that control the MRI hardware. To utilize an MRI scanner to the fullest extent, a conceptual understanding of its pulse sequences is crucial. This book offers a complete guide that can help the scientists, engineers, clinicians, and technologists in the field of MRI understand and better employ their scanner. ·Explains pulse sequences, their components, and the associated image reconstruction methods commonly used in MRI·Provides self-contained sections for individual techniques·Can be used as a quick reference guide or as a resource for deeper study·Includes both non-mathematical and mathematical descriptions ·Contains numerous figures, tables, references, and worked example problems

This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old; n = 171) and the test set (51.7 ± 11.3 years old; n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select radiomics features and calculate a risk score. A combined model was constructed by integrating the risk score with the clinical and pathological characteristics. The study identified two radiomics features (GLSZM and GLRLM) from DCE-MRI that were used to calculate a risk score. The AUCs were 0.860 and 0.868 in the training set and 0.816 and 0.714 in the testing set for 3- and 5-year recurrence risk, respectively. The combined model incorporating the risk score, pN, and endocrine therapy showed improved predictive power, with AUCs of 0.857 and 0.912 in the training set and 0.943 and 0.945 in the testing set for 3- and 5-year recurrence risk, respectively. The calibration curve of the combined model showed good consistency between predicted and measured values. Our study developed an MRI-based radiomics model that integrates clinical and radiomics features to assess the likelihood of recurrence in luminal B breast cancer. The model shows promise for improving clinical risk stratification and treatment decision-making.

Objective Pancreatic cancer is portrayed to become the second leading cause of cancer-related death within the next years. Potentially complicating surgical resection emphasizes the importance of an accurate TNM classification. In particular, the failure to detect features for non-resectability has profound consequences on patient outcomes and economic costs due to incorrect indication for resection. In the detection of liver metastases, contrast-enhanced MRI showed high sensitivity and specificity; however, the cost-effectiveness compared to the standard of care imaging remains unclear. The aim of this study was to analyze whether additional MRI of the liver is a cost-effective approach compared to routinely acquired contrast-enhanced computed tomography (CE-CT) in the initial staging of pancreatic cancer. Methods A decision model based on Markov simulation was developed to estimate the quality-adjusted life-years (QALYs) and lifetime costs of the diagnostic modalities. Model input parameters were assessed based on evidence from recent literature. The willingness-to-pay (WTP) was set to $100,000/QALY. To evaluate model uncertainty, deterministic and probabilistic sensitivity analyses were performed. Results In the base-case analysis, the model yielded a total cost of $185,597 and an effectiveness of 2.347 QALYs for CE-MR/CT and $187,601 and 2.337 QALYs for CE-CT respectively. With a net monetary benefit (NMB) of $49,133, CE-MR/CT is shown to be dominant over CE-CT with a NMB of $46,117. Deterministic and probabilistic survival analysis showed model robustness for varying input parameters. Conclusion Based on our results, combined CE-MR/CT can be regarded as a cost-effective imaging strategy for the staging of pancreatic cancer. Key Points • Additional MRI of the liver for initial staging of pancreatic cancer results in lower total costs and higher effectiveness. • The economic model showed high robustness for varying input parameters.

Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults ( N  = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort ( N  = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets. Genomic analyses of large population-based cohorts uncover the genetic determinants of perivascular space burden, an MRI marker of cerebral small vessel disease, across the lifespan, and reveal potential pathways implicated in the etiology of stroke and dementia.

Objectives To assess the utility of diffusion weighted imaging for monitoring early treatment effects associated with a VEGF inhibitor. Materials and methods Twenty-nine patients with metastatic abdominal and pelvic tumours were recruited and imaged with DW-MRI: twice at baseline, and after 7 and 28 days of treatment with cediranib. Tumour measures were derived using mono-exponential, bi-exponential and stretched-exponential models, and parameter repeatability and treatment effects seen after 7 and 28 days were assessed. Correlations with volume changes and DCE-MRI metrics were also assessed. Results Diffusion coefficient repeatabilities from all models were < 6 %; f and D* (bi-exponential) were 22 % and 44 %; α (stretched-exponential) was 4.2 %. Significant increases in the diffusion coefficients from all models were observed at day 28 but not day 7. Significant decreases in D* and f.D* were observed at day 7 and in f at day 28; significant increases in α were observed at both time-points. Weak correlations between DW-MRI changes and volume changes and DCE-MRI changes were observed. Conclusion DW-MRI is sensitive to early and late treatment changes caused by a VEGF inhibitor using non-mono-exponential models. Evidence of over-fitting using the bi-exponential model suggests that the stretched-exponential model is best suited to monitor such changes. Key points • Non-mono-exponential diffusion models widen sensitivity to a broader class of tissue properties. • A stretched-exponential model robustly detects changes after 7 days of VEGF-inhibitor treatment. • There are very weak correlations between DWI-IVIM perfusion and similar DCE-MRI measures. • Diffusion-weighted MRI is a highly informative technique for assessing novel tumour therapies.

Background and purpose The aim of this study was to assess the predictive potential of diffusion-weighted magnetic resonance imaging (MRI) for the selection of favorable pathological responders after radiochemotherapy for locally advanced rectal cancer. Patients and methods In 59 patients with locally advanced rectal cancer, the apparent diffusion coefficient (ADC) in the tumor was obtained at 3 Tesla before radiochemotherapy and surgery. The predictive potential for pathological complete response (pCR) and good response (GR) was assessed. GR was defined as pCR and near-pCR based on the tumor regression grade. Results The GR group consisted of 13 patients (22%) with 9 complete responders. Both the preradiochemotherapy ADC values and relative change in ADC (ΔADC) were predictive for pathological response. Preradiochemotherapy ADC values showed a positive predictive value of 42% for pCR and 67% for GR using a similar cut-off value of 0.97 * 10 −3  mm 2 /s. For ΔADC, the optimal threshold for predicting GR or pCR was a 41% increase of the ADC. With this threshold, positive predictive values of 64% and 91% were found for pCR and GR, respectively. Conclusion Low preradiochemotherapy ADC values and high ΔADC correspond to pathological good response. Diffusion-weighted MRI may be used as an additional tool for selecting good treatment responders after radiochemotherapy.

ObjectivesTo create a radiomics approach based on multiparametric magnetic resonance imaging (mpMRI) features extracted from an auto-fixed volume of interest (VOI) that quantifies the phenotype of clinically significant (CS) peripheral zone (PZ) prostate cancer (PCa).MethodsThis study included 206 patients with 262 prospectively called mpMRI prostate imaging reporting and data system 3–5 PZ lesions. Gleason scores > 6 were defined as CS PCa. Features were extracted with an auto-fixed 12-mm spherical VOI placed around a pin point in each lesion. The value of dynamic contrast-enhanced imaging(DCE), multivariate feature selection and extreme gradient boosting (XGB) vs. univariate feature selection and random forest (RF), expert-based feature pre-selection, and the addition of image filters was investigated using the training (171 lesions) and test (91 lesions) datasets.ResultsThe best model with features from T2-weighted (T2-w) + diffusion-weighted imaging (DWI) + DCE had an area under the curve (AUC) of 0.870 (95% CI 0.980–0.754). Removal of DCE features decreased AUC to 0.816 (95% CI 0.920–0.710), although not significantly (p = 0.119). Multivariate and XGB outperformed univariate and RF (p = 0.028). Expert-based feature pre-selection and image filters had no significant contribution.ConclusionsThe phenotype of CS PZ PCa lesions can be quantified using a radiomics approach based on features extracted from T2-w + DWI using an auto-fixed VOI. Although DCE features improve diagnostic performance, this is not statistically significant. Multivariate feature selection and XGB should be preferred over univariate feature selection and RF. The developed model may be a valuable addition to traditional visual assessment in diagnosing CS PZ PCa.Key Points• T2-weighted and diffusion-weighted imaging features are essential components of a radiomics model for clinically significant prostate cancer; addition of dynamic contrast-enhanced imaging does not significantly improve diagnostic performance.• Multivariate feature selection and extreme gradient outperform univariate feature selection and random forest.• The developed radiomics model that extracts multiparametric MRI features with an auto-fixed volume of interest may be a valuable addition to visual assessment in diagnosing clinically significant prostate cancer.