A site-wise reliability analysis of the ABCD diffusion fractional anisotropy and cortical thickness: impact of scanner platforms.

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce non-biological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61±0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76±0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared to those using GE/Philips scanners for both FA (AICC=0.71±0.12 vs 0.46±0.17, p<0.001) and CT (AICC=0.80±0.10 vs 0.69±0.11, p<0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

Probabilistic Presurgical Language Functional MRI Atlas of Brain Tumor Patients.

BACKGROUND AND PURPOSE: Patients with brain tumors have high intersubject variation in putative language regions, which may limit the utility of straightforward application of healthy-subject brain atlases in clinical scenarios. The purpose of this study was to develop a probabilistic functional brain atlas that consolidates language functional activations of sentence completion and silent word generation language paradigms using a large sample of patients with brain tumors. MATERIALS AND METHODS: The atlas was developed using retrospectively collected fMRI data from patients with brain tumors who underwent their first standard-of-care presurgical language fMRI scan at our institution between July 18, 2015, and May 13, 2022. 317 patients (861 fMRI scans) were used to develop the language functional atlas. An independent presurgical language fMRI dataset of 39 patients with brain tumors from a previous study was used to evaluate our atlas. Family-wise error corrected binary functional activation maps from sentence completion, letter fluency, and category fluency presurgical fMRI were used to create probability overlap maps and pooled probabilistic overlap map in Montreal Neurological Institute standard space. Wilcoxon signed-rank test was used to determine significant difference in the maximum Dice coefficient for our atlas compared to a meta-analysis-based template with respect to expert-delineated primary language area activations. RESULTS: Probabilities of activating left anterior primary language area and left posterior primary language area in temporal lobe were 87.9% and 91.5%, respectively, for sentence completion, 88.5% and 74.2%, respectively, for letter fluency, and 83.6% and 67.6%, respectively, for category fluency. Maximum Dice coefficients for templates derived from our language atlas were significantly higher than the meta-analysis-based template in left anterior primary language area (0.351 and 0.326, respectively, P < .05) and left posterior primary language area in temporal lobe (0.274 and 0.244, respectively, P < .005). CONCLUSIONS: Brain tumor patient-and paradigm-specific probabilistic language atlases were developed. These atlases had superior spatial agreement with fMRI activations in individual patients than the meta-analysis-based template. ABBREVIATIONS: SENT = sentence completion, LETT = letter fluency, CAT = category fluency, PLA = primary language area, aPLA = anterior PLA, pPLAT = posterior PLA in the temporal lobe, pPLAP = posterior PLA in the parietal lobe, SMA = supplementary motor area, DLPFC = dorsolateral prefrontal cortex, BTLA = basal temporal language area.

Comparative analysis of brain language templates with primary language areas detected from presurgical fMRI of brain tumor patients.

INTRODUCTION: Functional brain templates are often used in the analysis of clinical functional MRI (fMRI) studies. However, these templates are mostly built based on anatomy or fMRI of healthy subjects, which have not been fully vetted in clinical cohorts. Our aim was to evaluate language templates by comparing with primary language areas (PLAs) detected from presurgical fMRI of brain tumor patients. METHODS: Four language templates (A-D) based on anatomy, task-based fMRI, resting-state fMRI, and meta-analysis, respectively, were compared with PLAs detected by fMRI with word generation and sentence completion paradigms. For each template, the fraction of PLA activations enclosed by the template (positive inclusion fraction, [PIF]), the fraction of activations within the template but that did not belong to PLAs (false inclusion fraction, [FIF]), and their Dice similarity coefficient (DSC) with PLA activations were calculated. RESULTS: For anterior PLAs, Template A had the greatest PIF (median, 0.95), whereas Template D had both the lowest FIF (median, 0.074), and the highest DSC (median, 0.30), which were all significant compared to other templates. For posterior PLAs, Templates B and D had similar PIF (median, 0.91 and 0.90, respectively) and DSC (both medians, 0.059), which were all significantly higher than that of Template C. Templates B and C had significantly lower FIF (median, 0.061 and 0.054, respectively) compared to Template D. CONCLUSION: This study demonstrated significant differences between language templates in their inclusiveness of and spatial agreement with the PLAs detected in the presurgical fMRI of the patient cohort. These findings may help guide the selection of language templates tailored to their applications in clinical fMRI studies.

Predicting suicidality in late-life depression by 3D convolutional neural network and cross-sample entropy analysis of resting-state fMRI.

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality.

Intraoperative language mapping guided by real-time visualization of gamma band modulation electrocorticograms: Case report and proof of concept.

Background: Electrocorticography (ECoG) language mapping is often performed extraoperatively, frequently involves offline processing, and relationships with direct cortical stimulation (DCS) remain variable. We sought to determine the feasibility and preliminary utility of an intraoperative language mapping approach guided by real-time visualization of electrocorticograms. Methods: A patient with astrocytoma underwent awake craniotomy with intraoperative language mapping, utilizing a dual iPad stimulus presentation system coupled to a real-time neural signal processing platform capable of both ECoG recording and delivery of DCS. Gamma band modulations in response to 4 language tasks at each electrode were visualized in real-time. Next, DCS was conducted for each neighboring electrode pair during language tasks. Results: All language tasks resulted in strongest heat map activation at an electrode pair in the anterior to mid superior temporal gyrus. Consistent speech arrest during DCS was observed for Object and Action naming tasks at these same electrodes, indicating good correspondence with ECoG heat map recordings. This region corresponded well with posterior language representation via preoperative functional MRI. Conclusions: Intraoperative real-time visualization of language task-based ECoG gamma band modulation is feasible and may help identify targets for DCS. If validated, this may improve the efficiency and accuracy of intraoperative language mapping.

Cognitive protection and brain entropy changes from omega-3 polyunsaturated fatty acids supplement in late-life depression: A 52-week randomized controlled trial.

BACKGROUND: Late-life depression (LLD) is associated with risk of dementia, yet intervention of LLD provides an opportunity to attenuate subsequent cognitive decline. Omega-3 polyunsaturated fatty acids (PUFAs) supplement is a potential intervention due to their beneficial effect in depressive symptoms and cognitive function. To explore the underlying neural mechanism, we used resting-state functional MRI (rs-fMRI) before and after omega-3 PUFAs supplement in older adults with LLD. METHODS: A 52-week double-blind randomized controlled trial was conducted. We used multi-scale sample entropy to analyze rs-fMRI data. Comprehensive cognitive tests and inflammatory markers were collected to correlate with brain entropy changes. RESULTS: A total of 20 patients completed the trial with 11 under omega-3 PUFAs and nine under placebo. While no significant global cognitive improvement was observed, a marginal enhancement in processing speed was noted in the omega-3 PUFAs group. Importantly, participants receiving omega-3 PUFAs exhibited decreased brain entropy in left posterior cingulate gyrus (PCG), multiple visual areas, the orbital part of the right middle frontal gyrus, and the left Rolandic operculum. The brain entropy changes of the PCG in the omega-3 PUFAs group correlated with improvement of language function and attenuation of interleukin-6 levels. LIMITATIONS: Sample size is small with only marginal clinical effect. CONCLUSION: These findings suggest that omega-3 PUFAs supplement may mitigate cognitive decline in LLD through anti-inflammatory mechanisms and modulation of brain entropy. Larger clinical trials are warranted to validate the potential therapeutic implications of omega-3 PUFAs for deterring cognitive decline in patients with late-life depression.

Incidence of resting-state functional connectivity to secondary language areas and contralateral language areas in patients with brain tumors.

PURPOSE: Secondary language areas, including the pre-supplementary motor area (pre-SMA), dorsolateral prefrontal cortex (DLPFC), and the visual word form area (VWFA) play important roles in speech, but have been under-evaluated in the realm of resting-state (rs)-fMRI. The purpose of this study is to determine the incidence that secondary language areas and contralateral language areas can be localized using seed-based correlation (SBC) rs-fMRI. METHODS: We retrospectively reviewed 40 rs-fMRIs for functional connectivity (FC) to secondary language areas in cases where FC to Broca's or Wernicke's area near tumor in the left hemisphere were successfully generated using SBC analysis. Logistical regression was used for statistical analysis. RESULTS: SBC rs-fMRI with a seed in the left Broca's or Wernicke's area ipsilateral to the tumor was performed in the 40 patients. 72.5% of cases showed FC to the left DLPFC, 67.5% to left pre-SMA, and 52.5% of cases had FC to right Broca's area. In addition to other correlations, we found older patients have a lower incidence of FC to the right Wernicke's area when seeded from both left Broca's and left Wernicke's area (p-value = .016, odds ratio = 0.94). CONCLUSION: SBC rs-fMRI can detect left hemispheric secondary language areas as well as right hemispheric primary and secondary language areas. The left DLPFC showed the highest incidence of FC, followed by the left pre-SMA when seeded from both left Broca's and Wernicke's area. Logistics regression also showed in some instances, differences in the incidence of FC to language areas was dependent on age, seed location, and gender.

Automatic diagnosis of late-life depression by 3D convolutional neural networks and cross-sample Entropy analysis from resting-state fMRI.

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy > 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD.

Meta-analytic evidence for the cognitive control model of loneliness in emotion processing.

Loneliness is strongly related to affective dysregulation. However, the neuropsychological mechanisms underpinning the loneliness-affective processing relationships remain unclear. Here, we first utilised the coordinate-based activation likelihood estimation method to confirm functional clusters related to loneliness, including the striatum, superior and medial frontal gyrus, insula, and cuneus. Meta-analytic connectivity modelling was then performed to characterise the functional connectivity of these clusters across studies using emotion tasks. Our results revealed that these clusters co-activated with the cognitive control networks. From the literature, we understand that loneliness and its neural correlates are highly related to regulating the attention biases to social rewards and social cues. Therefore, our findings provide a proof-of-concept that loneliness up-regulates the cognitive control networks to process socio-affective information. Prolonged up-regulation thus exhausts cognitive resources and hence, affective dysregulation. This study offers insight into the intricate role of cognitive and affective regulation in loneliness and social perception and provides meta-analytic evidence of the cognitive control model of loneliness and loneliness-related affective dysregulation, bringing significant clinical implications.

Cerebrovascular Reactivity Mapping Using Resting-State Functional MRI in Patients With Gliomas.

BACKGROUND: Recently, a data-driven regression analysis method was developed to utilize the resting-state (rs) blood oxygenation level-dependent signal for cerebrovascular reactivity (CVR) mapping (rs-CVR), which was previously optimized by comparing with the CO2 inhalation-based method in health subjects and patients with neurovascular diseases. PURPOSE: To investigate the agreement of rs-CVR and the CVR mapping with breath-hold MRI (bh-CVR) in patients with gliomas. STUDY TYPE: Retrospective. POPULATION: Twenty-five patients (12 males, 13 females; mean age ± SD, 48 ± 13 years) with gliomas. FIELD STRENGTH/SEQUENCE: Dynamic T2*-weighted gradient-echo echo-planar imaging during a breath-hold paradigm and during the rs on a 3-T scanner. ASSESSMENT: rs-CVR with various frequency ranges and resting-state fluctuation amplitude (RSFA) were assessed. The agreement between each rs-based CVR measurement and bh-CVR was determined by voxel-wise correlation and Dice coefficient in the whole brain, gray matter, and the lesion region of interest (ROI). STATISTICAL TESTS: Voxel-wise Pearson correlation, Dice coefficient, Fisher Z-transformation, repeated-measure analysis of variance and post hoc test with Bonferroni correction, and nonparametric repeated-measure Friedman test and post hoc test with Bonferroni correction were used. Significance was set at P < 0.05. RESULTS: Compared with bh-CVR, the highest correlations were found at the frequency bands of 0.04-0.08 Hz and 0.02-0.04 Hz for rs-CVR in both whole brain and the lesion ROI. RSFA had significantly lower correlations than did rs-CVR of 0.02-0.04 Hz and a wider frequency range (0-0.1164 Hz). Significantly higher correlations and Dice coefficient were found in normal tissues than in the lesion ROI for all three methods. DATA CONCLUSION: The optimal frequency ranges for rs-CVR are determined by comparing with bh-CVR in patients with gliomas. The rs-CVR method outperformed the RSFA. Significantly higher correlation and Dice coefficient between rs- and bh-CVR were found in normal tissue than in the lesion. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Validation of Non-invasive Language Mapping Modalities for Eloquent Tumor Resection: A Pilot Study.

Many studies have established a link between extent of resection and survival in patients with gliomas. Surgeons must optimize the oncofunctional balance by maximizing the extent of resection and minimizing postoperative neurological morbidity. Preoperative functional imaging modalities are important tools for optimizing the oncofunctional balance. Transcranial magnetic stimulation (TMS) and functional magnetic resonance imaging (fMRI) are non-invasive imaging modalities that can be used for preoperative functional language mapping. Scarce data exist evaluating the accuracy of these preoperative modalities for language mapping compared with gold standard intraoperative data in the same cohort. This study compares the accuracy of fMRI and TMS for language mapping compared with intraoperative direct cortical stimulation (DCS). We also identified significant predictors of preoperative functional imaging accuracy, as well as significant predictors of functional outcomes. Evidence from this study could inform clinical judgment as well as provide neuroscientific insight. We used geometric distances to determine copositivity between preoperative data and intraoperative data. Twenty-eight patients were included who underwent both preoperative fMRI and TMS procedures, as well as an awake craniotomy and intraoperative language mapping. We found that TMS shows significantly superior correlation to intraoperative DCS compared with fMRI. TMS also showed significantly higher sensitivity and negative predictive value than specificity and positive predictive value. Poor cognitive baseline was associated with decreased TMS accuracy as well as increased risk for worsened aphasia postoperatively. TMS has emerged as a promising preoperative language mapping tool. Future work should be done to identify the proper role of each imaging modality in a comprehensive, multimodal approach to optimize the oncofunctional balance.

Neural substrates of respiratory sensory gating: A human fMRI study.

The involvement of neural substrates in respiratory sensory gating remained unclear. This study aimed to investigate cortical and subcortical activations associated with respiratory sensory gating by using functional magnetic resonance imaging. First, we hypothesized that paired occlusions would induce neural activation in cortical and subcortical areas, including the thalamus and sensorimotor cortices. Secondly, we hypothesized that, in terms of parameter estimates in the general linear model, the activation effect size ß ratios (ßpaired/ßsingle) would be less than 2 due to central neural gating mechanism. Forty-six healthy participants were included in the study. Our analyses showed that the ßpaired/ßsingle ratios for the supramarginal gyrus, basal ganglia, thalamus, and middle frontal gyrus were less than 2. In conclusion, our results demonstrated a non-linear relationship regarding brain neural activations in response to paired versus single occlusions, suggesting that respiratory sensory information is gated at the subcortical and cortical levels.

Multimodal Neural Evidence on the Corticostriatal Underpinning of Suicidality in Late-Life Depression.

BACKGROUND: Suicidality involves thoughts (ideations and plans) and actions related to self-inflicted death. To improve management and prevention of suicidality, it is essential to understand the key neural mechanisms underlying suicidal thoughts and actions. Following empirically informed neural framework, we hypothesized that suicidal thoughts would be primarily characterized by alterations in the default mode network indicating disrupted self-related processing, whereas suicidal actions would be characterized by changes in the lateral prefrontal corticostriatal circuitries implicating compromised action control. METHODS: We analyzed the gray matter volume and resting-state functional connectivity of 113 individuals with late-life depression, including 45 nonsuicidal patients, 33 with suicidal thoughts but no action, and 35 with past suicidal action. Between-group analyses revealed key neural features associated with suicidality. The functional directionality of the identified resting-state functional connectivity was examined using dynamic causal modeling to further elucidate its mechanistic nature. Post hoc classification analysis examined the contribution of the neural measures to suicide classification. RESULTS: As expected, reduced gray matter volumes in the default mode network and lateral prefrontal regions characterized patients with suicidal thoughts and those with past suicidal actions compared with nonsuicidal patients. Furthermore, region-of-interest analyses revealed that the directionality and strength of the ventrolateral prefrontal cortex-caudate resting-state functional connectivity were related to suicidal thoughts and actions. The neural features significantly improved classification of suicidal thoughts and actions over that based on clinical and suicide questionnaire variables. CONCLUSIONS: Gray matter reductions in the default mode network and lateral prefrontal regions and the ventrolateral prefrontal cortex-caudate connectivity alterations characterized suicidal thoughts and actions in patients with late-life depression.

Delayed FDG PET Provides Superior Glioblastoma Conspicuity Compared to Conventional Image Timing.

Background: Glioblastomas are malignant, often incurable brain tumors. Reliable discrimination between recurrent disease and treatment changes is a significant challenge. Prior work has suggested glioblastoma FDG PET conspicuity is improved at delayed time points vs. conventional imaging times. This study aimed to determine the ideal FDG imaging time point in a population of untreated glioblastomas in preparation for future trials involving the non-invasive assessment of true progression vs. pseudoprogression in glioblastoma. Methods: Sixteen pre-treatment adults with suspected glioblastoma received FDG PET at 1, 5, and 8 h post-FDG injection within the 3 days prior to surgery. Maximum standard uptake values were measured at each timepoint for the central enhancing component of the lesion and the contralateral normal-appearing brain. Results: Sixteen patients (nine male) had pathology confirmed IDH-wildtype, glioblastoma. Our results revealed statistically significant improvements in the maximum standardized uptake values and subjective conspicuity of glioblastomas at later time points compared to the conventional (1 h time point). The tumor to background ratio at 1, 5, and 8 h was 1.4 ± 0.4, 1.8 ± 0.5, and 2.1 ± 0.6, respectively. This was statistically significant for the 5 h time point over the 1 h time point (p > 0.001), the 8 h time point over the 1 h time point (p = 0.026), and the 8 h time point over the 5 h time point (p = 0.036). Conclusions: Our findings demonstrate that delayed imaging time point provides superior conspicuity of glioblastoma compared to conventional imaging. Further research based on these results may translate into improvements in the determination of true progression from pseudoprogression.

Changes of Brain Functional Connectivity in End-Stage Renal Disease Patients Receiving Peritoneal Dialysis Without Cognitive Decline.

Background: Functional connectivity detected by resting-state functional MRI (R-fMRI) helps to discover the subtle changes in brain activities. Patients with end-stage renal disease (ESRD) on hemodialysis (HD) have impaired brain networks. However, the functional changes of brain networks in patients with ESRD undergoing peritoneal dialysis (PD) have not been fully delineated, especially among those with preserved cognitive function. Therefore, it is worth knowing about the brain functional connectivity in patients with PD by using R-fMRI. Methods: This case-control study prospectively enrolled 19 patients with ESRD receiving PD and 24 age- and sex- matched controls. All participants without a history of cognitive decline received mini-mental status examination (MMSE) and brain 3-T R-fMRI. Comprehensive R-fMRI analyses included graph analysis for connectivity and seed-based correlation networks. Independent t-tests were used for comparing the graph parameters and connectivity networks between patients with PD and controls. Results: All subjects were cognitively intact (MMSE > 24). Whole-brain connectivity by graph analysis revealed significant differences between the two groups with decreased global efficiency (Eglob, p < 0.05), increased betweenness centrality (BC) (p < 0.01), and increased characteristic path length (L, p < 0.01) in patients with PD. The functional connections of the default-mode network (DMN), sensorimotor network (SMN), salience network (SN), and hippocampal network (HN) were impaired in patients with PD. Meanwhile, in DMN and SN, elevated connectivity was observed in certain brain regions of patients with PD. Conclusion: Patients with ESRD receiving PD had specific disruptions in functional connectivity. In graph analysis, Eglob, BC, and L showed significant connectivity changes compared to the controls. DMN and SN had the most prominent alterations among the observed networks, with both decreased and increased connectivity regions. Our study confirmed that significant changes in cerebral connections existed in cognitively intact patients with PD.

A dynamic susceptibility contrast MRI digital reference object for testing software with leakage correction: Effect of background simulation.

PURPOSE: Dynamic susceptibility contrast (DSC)-MRI is a perfusion imaging technique from which useful quantitative imaging biomarkers can be derived. Relative cerebral blood volume (rCBV) is such a biomarker commonly used for evaluating brain tumors. To account for the extravasation of contrast agents in tumors, post-processing leakage correction is often applied to improve rCBV accuracy. Digital reference objects (DRO) are ideal for testing the post-processing software, because the biophysical model used to generate the DRO can be matched to the one that the software uses. This study aims to develop DROs to validate the leakage correction of software using Weisskoff model and to examine the effect of background signal time curves that are required by the model. METHODS: Three DROs were generated using the Weisskoff model, each composed of nine foreground lesion objects with combinations of different levels of rCBV and contrast leakage parameter (K2). Three types of background were implemented for these DROs: (1) a multi-compartment brain-like background, (2) a sphere background with a constant signal time curve, and (3) a sphere background with signal time curve identical to that of the brain-like DRO's white matter (WM). The DROs were then analyzed with an FDA-cleared software with and without leakage correction. Leakage correction was tested with and without brain segmentation. RESULTS: Accuracy of leakage correction was able to be verified using the brain-like phantom and the sphere phantom with WM background. The sphere with constant background did not perform well with leakage correction with or without brain segmentation. The DROs were able to verify that for the particular software tested, leakage correction with brain segmentation achieved the lowest error. CONCLUSIONS: DSC-MRI DROs with biophysical model matched to that of the post-processing software can be well used for the software's validation, provided that the background signals are also properly simulated for generating the reference time curve required by the model. Care needs to be taken to consider the interaction of the design of the DRO with the software's implementation of brain segmentation to extract the reference time curve.

Greater white matter hyperintensities and the association with executive function in suicide attempters with late-life depression.

Late-life depression (LLD) is associated with greater risk of suicide and white matter hyperintensities (WMH), which are also found in suicide attempters regardless of age. Greater periventricular WMH are related to worse cognitive function. We investigated the spatial distribution of WMH in suicide attempters with LLD and its association with cognitive function. We recruited 114 participants with LLD (34 with history of suicide attempt and 80 without) and 47 older adult controls (individuals without LLD or history of suicide attempt). WMH were quantified by an automated segmentation algorithm and were classified into different regions. Suicide attempters with LLD had significantly higher global WMH (F3, 150 = 2.856, p = 0.039) and periventricular WMH (F3, 150 = 3.635, p = 0.014) compared to other groups. Suicide attempters with high WMH had significantly lower executive function, which could be an underlying mechanism for cognitive decline in older adults with suicidality.

Effect of brain normalization methods on the construction of functional connectomes from resting-state functional MRI in patients with gliomas.

PURPOSE: Spatial normalization is an essential step in resting-state functional MRI connectomic analysis with atlas-based parcellation, but brain lesions can confound it. Cost-function masking (CFM) is a popular compensation approach, but may not benefit modern normalization methods. This study compared three normalization methods with and without CFM and determined their impact on connectomic measures in patients with glioma. METHODS: Fifty patients with glioma were included. T1 -weighted images were normalized using three different methods in SPM12, with and without CFM, which were then overlaid on the ICBM152 template and scored by two neuroradiologists. The Dice coefficient of gray-matter correspondence was also calculated. Normalized resting-state functional MRI data were parcellated using the AAL90 atlas to construct an individual connectivity matrix and calculate connectomic measures. The R2 among the different normalization methods was calculated for the connectivity matrices and connectomic measures. RESULTS: The older method (Original) performed significantly worse than the modern methods (Default and DARTEL; P < .005 in observer ranking). The use of CFM did not significantly improve the normalization results. The Original method had lower correlation with the Default and DARTEL methods (R2 = 0.71-0.74) than Default with DARTEL (R2 = 0.96) in the connectivity matrix. The clustering coefficient appears to be the most, and modularity the least, sensitive connectomic measures to normalization performance. CONCLUSION: The spatial normalization method can have an impact on resting-state functional MRI connectome and connectomic measures derived using atlas-based brain parcellation. In patients with glioma, this study demonstrated that Default and DARTEL performed better than the Original method, and that CFM made no significant difference.

Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors.

PURPOSE: Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS: One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS: Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION: Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.