Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

How do AI markings on screening mammograms correspond to cancer location? An informed review of 270 breast cancer cases in BreastScreen Norway.

OBJECTIVES: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. METHODS: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. RESULTS: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. CONCLUSION: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. CLINICAL RELEVANCE STATEMENT: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. KEY POINTS: • All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. • Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. • Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.

AI performance by mammographic density in a retrospective cohort study of 99,489 participants in BreastScreen Norway.

OBJECTIVE: To explore the ability of artificial intelligence (AI) to classify breast cancer by mammographic density in an organized screening program. MATERIALS AND METHOD: We included information about 99,489 examinations from 74,941 women who participated in BreastScreen Norway, 2013-2019. All examinations were analyzed with an AI system that assigned a malignancy risk score (AI score) from 1 (lowest) to 10 (highest) for each examination. Mammographic density was classified into Volpara density grade (VDG), VDG1-4; VDG1 indicated fatty and VDG4 extremely dense breasts. Screen-detected and interval cancers with an AI score of 1-10 were stratified by VDG. RESULTS: We found 10,406 (10.5% of the total) examinations to have an AI risk score of 10, of which 6.7% (704/10,406) was breast cancer. The cancers represented 89.7% (617/688) of the screen-detected and 44.6% (87/195) of the interval cancers. 20.3% (20,178/99,489) of the examinations were classified as VDG1 and 6.1% (6047/99,489) as VDG4. For screen-detected cancers, 84.0% (68/81, 95% CI, 74.1-91.2) had an AI score of 10 for VDG1, 88.9% (328/369, 95% CI, 85.2-91.9) for VDG2, 92.5% (185/200, 95% CI, 87.9-95.7) for VDG3, and 94.7% (36/38, 95% CI, 82.3-99.4) for VDG4. For interval cancers, the percentages with an AI score of 10 were 33.3% (3/9, 95% CI, 7.5-70.1) for VDG1 and 48.0% (12/25, 95% CI, 27.8-68.7) for VDG4. CONCLUSION: The tested AI system performed well according to cancer detection across all density categories, especially for extremely dense breasts. The highest proportion of screen-detected cancers with an AI score of 10 was observed for women classified as VDG4. CLINICAL RELEVANCE STATEMENT: Our study demonstrates that AI can correctly classify the majority of screen-detected and about half of the interval breast cancers, regardless of breast density. KEY POINTS: • Mammographic density is important to consider in the evaluation of artificial intelligence in mammographic screening. • Given a threshold representing about 10% of those with the highest malignancy risk score by an AI system, we found an increasing percentage of cancers with increasing mammographic density. • Artificial intelligence risk score and mammographic density combined may help triage examinations to reduce workload for radiologists.

Leveraging machine learning to identify subgroups of misclassified patients in the emergency department: a multi-center proof-of-concept study.

BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage or waste of hospital resources in the case of overtriage. Recent advances in machine learning algorithms allow for the quantification of variables important to under- and overtriage. OBJECTIVE: The aim of this study was to identify clinical features most strongly associated with triage misclassification using a machine learning classification model to capture non-linear relationships. METHODS: Multicenter retrospective cohort data from two big regional hospitals in Norway was extracted. The South African Triage system is used at Bergen University Hospital and the Rapid Emergency Triage and Treatment System is used at Trondheim University Hospital. Variables included triage score, age, gendersex, arrival time, subject area affiliation, reason for emergency department contact, discharge location, level of care, and time of death were retrieved. Random forest classification models were used to identify features with the strongest association with overtriage and undertriage in clinical practice in Bergen and Trondheim. We reported variable importance as SHAP-values (SHapley Additive exPlanations). RESULTS: We collected data on 205,488 patient records from Bergen University Hospital and 304,997 patient records from Trondheim University Hospital. Overall, overtriage was very uncommon at both hospitals (all <0.1%), with undertriage differing between both locationlocations with 0.8% at Bergen and 0.2% at Trondheim University Hospital. Demographics were similar for both hospitals, however the percentage given a high priority triage score (red or orange) was higher in Bergen (24%) compared to 9% in Trondheim. Clinical referral department was found to be the variable with the strongest association with undertriage (mean SHAP +0.62 and +0.37 for Bergen and Trondheim, respectively). CONCLUSIONS: We identified subgroups of patients consistently undertriaged using two common triage systems. While the importance of clinical characteristics to triage misclassification varies by triage system and location, but we found consistent evidence between the two locations that clinical referral department is the most important variable associated with triage misclassification. Replication of this approach at other centers could help to further improve triage scoring systems and improve patient care worldwide.

Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases.

OBJECTIVES: To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. METHODS: In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratified by mammographic density. RESULTS: A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. CONCLUSIONS: The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading. KEY POINTS: • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.

Neurobiological mechanisms of ECT and TMS treatment in depression: study protocol of a multimodal magnetic resonance investigation.

BACKGROUND: Noninvasive neurostimulation treatments are increasingly being used to treat major depression, which is a common cause of disability worldwide. While electroconvulsive therapy (ECT) and transcranial magnetic stimulation (TMS) are both effective in treating depressive episodes, their mechanisms of action are, however, not completely understood. ECT is given under general anesthesia, where an electrical pulse is administered through electrodes placed on the patient's head to trigger a seizure. ECT is used for the most severe cases of depression and is usually not prescribed before other options have failed. With TMS, brain stimulation is achieved through rapidly changing magnetic fields that induce electric currents underneath a ferromagnetic coil. Its efficacy in depressive episodes has been well documented. This project aims to identify the neurobiological underpinnings of both the effects and side effects of the neurostimulation techniques ECT and TMS. METHODS: The study will utilize a pre-post case control longitudinal design. The sample will consist of 150 subjects: 100 patients (bipolar and major depressive disorder) who are treated with either ECT (N = 50) or TMS (N = 50) and matched healthy controls (N = 50) not receiving any treatment. All participants will undergo multimodal magnetic resonance imaging (MRI) as well as neuropsychological and clinical assessments at multiple time points before, during and after treatment. Arterial spin labeling MRI at baseline will be used to test whether brain perfusion can predict outcomes. Signs of brain disruption, potentiation and rewiring will be explored with resting-state functional MRI, magnetic resonance spectroscopy and multishell diffusion weighted imaging (DWI). Clinical outcome will be measured by clinician assessed and patient reported outcome measures. Memory-related side effects will be investigated, and specific tests of spatial navigation to test hippocampal function will be administered both before and after treatment. Blood samples will be stored in a biobank for future analyses. The observation time is 6 months. Data will be explored in light of the recently proposed disrupt, potentiate and rewire (DPR) hypothesis. DISCUSSION: The study will contribute data and novel analyses important for our understanding of neurostimulation as well as for the development of enhanced and more personalized treatment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05135897.

Arterial input functions in dynamic susceptibility contrast MRI (DSC-MRI) in longitudinal evaluation of brain metastases.

BACKGROUND: Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) could be helpful to separate true disease progression from pseudo-progression in brain metastases when assessing the need for retreatment. However, the selection of arterial input functions (AIFs) is not standardized for analysis, limiting its use for this application. PURPOSE: To compare population-based AIFs, AIFs specific to each patient, and AIFs specific to every visit in the longitudinal follow-up of brain metastases. MATERIAL AND METHODS: Longitudinal data were collected from eight patients before treatment (6 of 8 patients) and after treatment (6-17 visits). Imaging was performed using a 1.5-T MRI system. Lesions were segmented by subtracting precontrast images from postcontrast images. Cerebral blood volume (rCBV) and cerebral blood flow (rCBF) were computed, and Pearson's product moment correlation coefficients were calculated to evaluate similarity of DSC parameters dependent on various AIF choices across time. AIF shape characteristics were compared. Parameter differences between white matter (WM) and gray matter (GM) were obtained to determine which AIF choice maximizes tissue differentiation. RESULTS: Although DSC parameters follow similar patterns in time, the various AIF selections cause large parameter variations with relative standard deviations of up to ±60%. AIFs sampled in one patient across sessions more similar in shape than AIFs sampled across patients. Estimates of rCBV based on scan-specific AIFs differentiated better between perfusion in WM and GM than patient-specific or population-based AIFs (P ≤ 0.02). CONCLUSION: Results indicate that scan-specific AIFs are the best choice for DSC-MRI parameter estimations in the longitudinal follow-up of brain metastases.

Characterization of the diffusion signal of breast tissues using multi-exponential models.

PURPOSE: Restriction spectrum imaging (RSI) decomposes the diffusion-weighted MRI signal into separate components of known apparent diffusion coefficients (ADCs). The number of diffusion components and optimal ADCs for RSI are organ-specific and determined empirically. The purpose of this work was to determine the RSI model for breast tissues. METHODS: The diffusion-weighted MRI signal was described using a linear combination of multiple exponential components. A set of ADC values was estimated to fit voxels in cancer and control ROIs. Later, the signal contributions of each diffusion component were estimated using these fixed ADC values. Relative-fitting residuals and Bayesian information criterion were assessed. Contrast-to-noise ratio between cancer and fibroglandular tissue in RSI-derived signal contribution maps was compared to DCE imaging. RESULTS: A total of 74 women with breast cancer were scanned at 3.0 Tesla MRI. The fitting residuals of conventional ADC and Bayesian information criterion suggest that a 3-component model improves the characterization of the diffusion signal over a biexponential model. Estimated ADCs of triexponential model were D1,3 = 0, D2,3 = 1.5 × 10-3 , and D3,3 = 10.8 × 10-3 mm2 /s. The RSI-derived signal contributions of the slower diffusion components were larger in tumors than in fibroglandular tissues. Further, the contrast-to-noise and specificity at 80% sensitivity of DCE and a subset of RSI-derived maps were equivalent. CONCLUSION: Breast diffusion-weighted MRI signal was best described using a triexponential model. Tumor conspicuity in breast RSI model is comparable to that of DCE without the use of exogenous contrast. These data may be used as differential features between healthy and malignant breast tissues.

Recommendations for Identifying Valid Wear for Consumer-Level Wrist-Worn Activity Trackers and Acceptability of Extended Device Deployment in Children.

BACKGROUND: Self-reported physical activity is often inaccurate. Wearable devices utilizing multiple sensors are now widespread. The aim of this study was to determine acceptability of Fitbit Charge HR for children and their families, and to determine best practices for processing its objective data. METHODS: Data were collected via Fitbit Charge HR continuously over the course of 3 weeks. Questionnaires were given to each child and their parent/guardian to determine the perceived usability of the device. Patterns of data were evaluated and best practice inclusion criteria recommended. RESULTS: Best practices were established to extract, filter, and process data to evaluate device wear, r and establish minimum wear time to evaluate behavioral patterns. This resulted in usable data available from 137 (89%) of the sample. CONCLUSIONS: Activity trackers are highly acceptable in the target population and can provide objective data over longer periods of wear. Best practice inclusion protocols that reflect physical activity in youth are provided.

Meaningful associations in the adolescent brain cognitive development study.

The Adolescent Brain Cognitive Development (ABCD) Study is the largest single-cohort prospective longitudinal study of neurodevelopment and children's health in the United States. A cohort of n = 11,880 children aged 9-10 years (and their parents/guardians) were recruited across 22 sites and are being followed with in-person visits on an annual basis for at least 10 years. The study approximates the US population on several key sociodemographic variables, including sex, race, ethnicity, household income, and parental education. Data collected include assessments of health, mental health, substance use, culture and environment and neurocognition, as well as geocoded exposures, structural and functional magnetic resonance imaging (MRI), and whole-genome genotyping. Here, we describe the ABCD Study aims and design, as well as issues surrounding estimation of meaningful associations using its data, including population inferences, hypothesis testing, power and precision, control of covariates, interpretation of associations, and recommended best practices for reproducible research, analytical procedures and reporting of results.

Correction of Artifacts Induced by B0 Inhomogeneities in Breast MRI Using Reduced-Field-of-View Echo-Planar Imaging and Enhanced Reversed Polarity Gradient Method.

BACKGROUND: Diffusion-weighted (DW) echo-planar imaging (EPI) is prone to geometric distortions due to B0 inhomogeneities. Both prospective and retrospective approaches have been developed to decrease and correct such distortions. PURPOSE: The purpose of this work was to evaluate the performance of reduced-field-of-view (FOV) acquisition and retrospective distortion correction methods in decreasing distortion artifacts for breast imaging. Coverage of the axilla in reduced-FOV DW magnetic resonance imaging (MRI) and residual distortion were also assessed. STUDY TYPE: Retrospective. POPULATION/PHANTOM: Breast phantom and 169 women (52.4 ± 13.4 years old) undergoing clinical breast MRI. FIELD STRENGTH/SEQUENCE: A 3.0 T/ full- and reduced-FOV DW gradient-echo EPI sequence. ASSESSMENT: Performance of reversed polarity gradient (RPG) and FSL topup in correcting breast full- and reduced-FOV EPI data was evaluated using the mutual information (MI) metric between EPI and anatomical images. Two independent breast radiologists determined if coverage on both EPI data sets was adequate to evaluate axillary nodes and identified residual nipple distortion artifacts. STATISTICAL TESTS: Two-way repeated-measures analyses of variance and post hoc tests were used to identify differences between EPI modality and distortion correction method. Generalized linear mixed effects models were used to evaluate differences in axillary coverage and residual nipple distortion. RESULTS: In a breast phantom, residual distortions were 0.16 ± 0.07 cm and 0.22 ± 0.13 cm in reduced- and full-FOV EPI with both methods, respectively. In patients, MI significantly increased after distortion correction of full-FOV (11 ± 5% and 18 ± 9%, RPG and topup) and reduced-FOV (8 ± 4% both) EPI data. Axillary nodes were observed in 99% and 69% of the cases in full- and reduced-FOV EPI images. Residual distortion was observed in 93% and 0% of the cases in full- and reduced-FOV images. DATA CONCLUSION: Minimal distortion was achieved with RPG applied to reduced-FOV EPI data. RPG improved distortions for full-FOV images but with more modest improvements and limited correction near the nipple. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Elevated body weight modulates subcortical volume change and associated clinical response following electroconvulsive therapy.

Background: Obesity is a frequent somatic comorbidity of major depression, and it has been associated with worse clinical outcomes and brain structural abnormalities. Converging evidence suggests that electroconvulsive therapy (ECT) induces both clinical improvements and increased subcortical grey matter volume in patients with depression. However, it remains unknown whether increased body weight modulates the clinical response and structural neuroplasticity that occur with ECT. Methods: To address this question, we conducted a longitudinal investigation of structural MRI data from the Global ECT-MRI Research Collaboration (GEMRIC) in 223 patients who were experiencing a major depressive episode (10 scanning sites). Structural MRI data were acquired before and after ECT, and we assessed change in subcortical grey matter volume using FreeSurfer and Quarc. Results: Higher body mass index (BMI) was associated with a significantly lower increase in subcortical grey matter volume following ECT. We observed significant negative associations between BMI and change in subcortical grey matter volume, with pronounced effects in the thalamus and putamen, where obese participants showed increases in grey matter volume that were 43.3% and 49.6%, respectively, of the increases found in participants with normal weight. As well, BMI significantly moderated the association between subcortical grey matter volume change and clinical response to ECT. We observed no significant association between BMI and clinical response to ECT. Limitations: Because only baseline BMI values were available, we were unable to study BMI changes during ECT and their potential association with clinical and grey matter volume change. Conclusion: Future studies should take into account the relevance of body weight as a modulator of structural neuroplasticity during ECT treatment and aim to further explore the functional relevance of this novel finding.

Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.

The adolescent brain undergoes profound structural changes which is influenced by many factors. Screen media activity (SMA; e.g., watching television or videos, playing video games, or using social media) is a common recreational activity in children and adolescents; however, its effect on brain structure is not well understood. A multivariate approach with the first cross-sectional data release from the Adolescent Brain Cognitive Development (ABCD) study was used to test the maturational coupling hypothesis, i.e. the notion that coordinated patterns of structural change related to specific behaviors. Moreover, the utility of this approach was tested by determining the association between these structural correlation networks and psychopathology or cognition. ABCD participants with usable structural imaging and SMA data (N = 4277 of 4524) were subjected to a Group Factor Analysis (GFA) to identify latent variables that relate SMA to cortical thickness, sulcal depth, and gray matter volume. Subject scores from these latent variables were used in generalized linear mixed-effect models to investigate associations between SMA and internalizing and externalizing psychopathology, as well as fluid and crystalized intelligence. Four SMA-related GFAs explained 37% of the variance between SMA and structural brain indices. SMA-related GFAs correlated with brain areas that support homologous functions. Some but not all SMA-related factors corresponded with higher externalizing (Cohen's d effect size (ES) 0.06-0.1) but not internalizing psychopathology and lower crystalized (ES: 0.08-0.1) and fluid intelligence (ES: 0.04-0.09). Taken together, these findings support the notion of SMA related maturational coupling or structural correlation networks in the brain and provides evidence that individual differences of these networks have mixed consequences for psychopathology and cognitive performance.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study.

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis.

BACKGROUND: Restriction spectrum imaging (RSI) is a recently introduced magnetic resonance imaging diffusion technique. The utility of RSI in multiple sclerosis (MS) is unknown. OBJECTIVE: To investigate the association between RSI-derived parameters and neurological disability in MS. METHODS: Seventy-seven relapsing-remitting MS patients were scanned with RSI on a 3-T scanner. RSI-derived parameters: fast and slow apparent diffusion coefficient (sADC), fractional anisotropy, restricted fractional anisotropy, neurite density (ND), cellularity, extracellular water fraction, and free water fraction, were obtained in white matter lesions (WML) and normal appearing white matter (NAWM). Patients were divided into three groups according to their expanded disability status scale (EDSS): with minimal, low, and substantial disability (<2.5, 2.5-3, and >3, respectively). Group comparisons and correlation analyses were performed. RESULTS: All tested RSI-derived parameters differed between WML and NAWM ( p < 0.001 for all pairwise comparisons). The sADC in WML showed largest difference across disability subgroups (analysis of variance (ANOVA): F = 5.1, η2 = 0.12, p = 0.008). ND in NAWM showed strongest correlation with disability (ϱ = -0.39, p < 0.001). CONCLUSION: The strongest correlation with EDSS of ND obtained in NAWM indicates that processes outside lesions are important for disability in MS. Our study suggests that RSI-derived parameters may help understand the "clinico-radiological paradox" and improve disease monitoring in MS.

Conservation of Distinct Genetically-Mediated Human Cortical Pattern.

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10-3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.

Neurodevelopmental origins of lifespan changes in brain and cognition.

Neurodevelopmental origins of functional variation in older age are increasingly being acknowledged, but identification of how early factors impact human brain and cognition throughout life has remained challenging. Much focus has been on age-specific mechanisms affecting neural foundations of cognition and their change. In contrast to this approach, we tested whether cerebral correlates of general cognitive ability (GCA) in development could be extended to the rest of the lifespan, and whether early factors traceable to prenatal stages, such as birth weight and parental education, may exert continuous influences. We measured the area of the cerebral cortex in a longitudinal sample of 974 individuals aged 4-88 y (1,633 observations). An extensive cortical region was identified wherein area related positively to GCA in development. By tracking area of the cortical region identified in the child sample throughout the lifespan, we showed that the cortical change trajectories of higher and lower GCA groups were parallel through life, suggesting continued influences of early life factors. Birth weight and parental education obtained from the Norwegian Mother-Child Cohort study were identified as such early factors of possible life-long influence. Support for a genetic component was obtained in a separate twin sample (Vietnam Era Twin Study of Aging), but birth weight in the child sample had an effect on cortical area also when controlling for possible genetic differences in terms of parental height. Our results provide novel evidence for stability in brain-cognition relationships throughout life, and indicate that early life factors impact brain and cognition for the entire life course.

Development and aging of cortical thickness correspond to genetic organization patterns.

There is a growing realization that early life influences have lasting impact on brain function and structure. Recent research has demonstrated that genetic relationships in adults can be used to parcellate the cortex into regions of maximal shared genetic influence, and a major hypothesis is that genetically programmed neurodevelopmental events cause a lasting impact on the organization of the cerebral cortex observable decades later. Here we tested how developmental and lifespan changes in cortical thickness fit the underlying genetic organizational principles of cortical thickness in a longitudinal sample of 974 participants between 4.1 and 88.5 y of age with a total of 1,633 scans, including 773 scans from children below 12 y. Genetic clustering of cortical thickness was based on an independent dataset of 406 adult twins. Developmental and adult age-related changes in cortical thickness followed closely the genetic organization of the cerebral cortex, with change rates varying as a function of genetic similarity between regions. Cortical regions with overlapping genetic architecture showed correlated developmental and adult age change trajectories and vice versa for regions with low genetic overlap. Thus, effects of genes on regional variations in cortical thickness in middle age can be traced to regional differences in neurodevelopmental change rates and extrapolated to further adult aging-related cortical thinning. This finding suggests that genetic factors contribute to cortical changes through life and calls for a lifespan perspective in research aimed at identifying the genetic and environmental determinants of cortical development and aging.

Restriction spectrum imaging: An evolving imaging biomarker in prostate MRI.

Restriction spectrum imaging (RSI) is a novel diffusion-weighted MRI technique that uses the mathematically distinct behavior of water diffusion in separable microscopic tissue compartments to highlight key aspects of the tissue microarchitecture with high conspicuity. RSI can be acquired in less than 5 min on modern scanners using a surface coil. Multiple field gradients and high b-values in combination with postprocessing techniques allow the simultaneous resolution of length-scale and geometric information, as well as compartmental and nuclear volume fraction filtering. RSI also uses a distortion correction technique and can thus be fused to high resolution T2-weighted images for detailed localization, which improves delineation of disease extension into critical anatomic structures. In this review, we discuss the acquisition, postprocessing, and interpretation of RSI for prostate MRI. We also summarize existing data demonstrating the applicability of RSI for prostate cancer detection, in vivo characterization, localization, and targeting. LEVEL OF EVIDENCE: 5 J. Magn. Reson. Imaging 2017;45:323-336.

Steeper Slope of Age-Related Changes in White Matter Microstructure and Processing Speed in Bipolar Disorder.

OBJECTIVES: Bipolar disorder (BD) is associated with compromised white matter (WM) integrity and deficits in processing speed (PS). Few studies, however, have investigated age relationships with WM structure and cognition to understand possible changes in brain health over the lifespan. This investigation explored whether BD and healthy counterpart (HC) participants exhibited differential age-related associations with WM and cognition, which may be suggestive of accelerated brain and cognitive aging. DESIGN: Cross-sectional study. SETTING: University of California San Diego and the Veterans Administration San Diego Healthcare System. PARTICIPANTS: 33 euthymic BD and 38 HC participants. MEASUREMENTS: Diffusion tensor imaging was acquired as a measure of WM integrity, and tract-specific fractional anisotropy (FA) was extracted utilizing the Johns Hopkins University probability atlas. PS was assessed with the Number and Letter Sequencing conditions of the Delis-Kaplan Executive Function System Trail Making Test. RESULTS: BD participants demonstrated slower PS compared with the HC group, but no group differences were found in FA across tracts. Multiple linear regressions revealed a significant group-by-age interaction for the right uncinate fasciculus, the left hippocampal portion of the cingulum, and for PS, such that older age was associated with lower FA values and slower PS in the BD group only. The relationship between age and PS did not significantly change after accounting for uncinate FA, suggesting that the observed age associations occur independently. CONCLUSIONS: Results provide support for future study of the accelerated aging hypothesis by identifying markers of brain health that demonstrate a differential age association in BD.