Astrogliosis, neuritic microstructure, and sex effects: GFAP is an indicator of neuritic orientation in women.

BACKGROUND: Data from human studies suggest that immune dysregulation is associated with Alzheimer's disease (AD) pathology and cognitive decline and that neurites may be affected early in the disease trajectory. Data from animal studies further indicate that dysfunction in astrocytes and inflammation may have a pivotal role in facilitating dendritic damage, which has been linked with negative cognitive outcomes. To elucidate these relationships further, we have examined the relationship between astrocyte and immune dysregulation, AD-related pathology, and neuritic microstructure in AD-vulnerable regions in late life. METHODS: We evaluated panels of immune, vascular, and AD-related protein markers in blood and conducted in vivo multi-shell neuroimaging using Neurite Orientation Dispersion and Density Imaging (NODDI) to assess indices of neuritic density (NDI) and dispersion (ODI) in brain regions vulnerable to AD in a cohort of older adults (n = 109). RESULTS: When examining all markers in tandem, higher plasma GFAP levels were strongly related to lower neurite dispersion (ODI) in grey matter. No biomarker associations were found with higher neuritic density. Associations between GFAP and neuritic microstructure were not significantly impacted by symptom status, APOE status, or plasma Aß42/40 ratio; however, there was a large sex effect observed for neurite dispersion, wherein negative associations between GFAP and ODI were only observed in females. DISCUSSION: This study provides a comprehensive, concurrent appraisal of immune, vascular, and AD-related biomarkers in the context of advanced grey matter neurite orientation and dispersion methodology. Sex may be an important modifier of the complex associations between astrogliosis, immune dysregulation, and brain microstructure in older adults.

Posterior white matter integrity and self-reported posterior cortical symptoms using the Colorado Posterior Cortical Questionnaire.

Background: The Colorado Posterior Cortical Questionnaire (CPC-Q) is a self-report, 15-item screening questionnaire for posterior cortical symptoms, including visuospatial and visuoperceptual difficulties. Changes in white matter connectivity may precede obvious gray matter atrophy in neurodegenerative conditions, especially posterior cortical atrophy. Integration of CPC-Q scores and measures of white matter integrity could contribute to earlier detection of posterior cortical syndromes. Methods: We investigated the relationships between posterior cortical symptoms as captured by the CPC-Q and diffusion tensor imaging fractional anisotropy (DTI FA) of white matter regions of interest localized to posterior brain regions (posterior thalamic radiations, splenium of corpus callosum, tapetum). Comparisons were also made by diagnostic group [healthy older adult (n = 31), amnestic Alzheimer's disease (AD, n = 18), and posterior cortical atrophy (PCA, n = 9)] and by SENAS battery visuospatial composite score quartile. Exploratory comparisons of all available individual white matter region DTI FA to CPC-Q, as well as comparisons of DTI FA between diagnostic groups and visuospatial quartiles, were also made. Results: CPC-Q score was correlated with the average DTI FA for the averaged posterior white matter regions of interest (r = -0.31, p = 0.02). Posterior thalamic radiation DTI FA was most strongly associated with CPC-Q (r = -0.34, p = 0.01) and visuospatial composite (r = 0.58, p < 0.01) scores and differed between the PCA and AD groups and the lower and higher visuospatial quartiles. The DTI FA of body and splenium of the corpus callosum also demonstrated this pattern but not the DTI FA of the tapetum. Conclusion: The integrity of posterior white matter tracts is associated with scores on the CPC-Q, adding to the validation evidence for this new questionnaire. White matter regions that may be related to posterior cortical symptoms detected by the CPC-Q, and distinct from those affected in amnestic syndromes, include the posterior thalamic radiations and body and splenium of the corpus callosum. These findings are in line with previous neuroimaging studies of PCA and support continued research on white matter in posterior cortical dysfunction.

Pancreatic fat relates to fasting insulin and postprandial lipids but not polycystic ovary syndrome in adolescents with obesity.

OBJECTIVE: Adolescents with polycystic ovary syndrome (PCOS) and obesity can have insulin resistance, dysglycemia, and hepatic steatosis. Excess pancreatic fat may disturb insulin secretion and relate to hepatic fat. Associations between pancreatic fat fraction (PFF) and metabolic measures in PCOS were unknown. METHODS: This secondary analysis included 113 sedentary, nondiabetic adolescent girls (age = 15.4 [1.9] years), with or without PCOS and BMI ≥ 90th percentile. Participants underwent fasting labs, oral glucose tolerance tests, and magnetic resonance imaging for hepatic fat fraction (HFF) and PFF. Groups were categorized by PFF (above or below the median of 2.18%) and compared. RESULTS: Visceral fat and HFF were elevated in individuals with PCOS versus control individuals, but PFF was similar. PFF did not correlate with serum androgens. Higher and lower PFF groups had similar HFF, with no correlation between PFF and HFF, although hepatic steatosis was more common in those with higher PFF (≥5.0% HFF; 60% vs. 36%; p = 0.014). The higher PFF group had higher fasting insulin (p = 0.026), fasting insulin resistance (homeostatic model assessment of insulin resistance, p = 0.032; 1/fasting insulin, p = 0.028), free fatty acids (p = 0.034), and triglycerides (p = 0.004) compared with those with lower PFF. ß-Cell function and insulin sensitivity were similar between groups. CONCLUSIONS: Neither PCOS status nor androgens related to PFF. However, fasting insulin and postprandial lipids were worse with higher PFF.

Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Coding of level of ambiguity within neural systems mediating choice.

Data from previous neuroimaging studies exploring neural activity associated with uncertainty suggest varying levels of activation associated with changing degrees of uncertainty in neural regions that mediate choice behavior. The present study used a novel task that parametrically controlled the amount of information hidden from the subject; levels of uncertainty ranged from full ambiguity (no information about probability of winning) through multiple levels of partial ambiguity, to a condition of risk only (zero ambiguity with full knowledge of the probability of winning). A parametric analysis compared a linear model in which weighting increased as a function of level of ambiguity, and an inverted-U quadratic models in which partial ambiguity conditions were weighted most heavily. Overall we found that risk and all levels of ambiguity recruited a common "fronto-parietal-striatal" network including regions within the dorsolateral prefrontal cortex, intraparietal sulcus, and dorsal striatum. Activation was greatest across these regions and additional anterior and superior prefrontal regions for the quadratic function which most heavily weighs trials with partial ambiguity. These results suggest that the neural regions involved in decision processes do not merely track the absolute degree ambiguity or type of uncertainty (risk vs. ambiguity). Instead, recruitment of prefrontal regions may result from greater degree of difficulty in conditions of partial ambiguity: when information regarding reward probabilities important for decision making is hidden or not easily obtained the subject must engage in a search for tractable information. Additionally, this study identified regions of activity related to the valuation of potential gains associated with stimuli or options (including the orbitofrontal and medial prefrontal cortices and dorsal striatum) and related to winning (including orbitofrontal cortex and ventral striatum).

Interactions within and between corticostriatal loops during component processes of category learning.

We examined dynamic interactions between cortex and BG during stimulus-response and feedback processing phases of categorization. First, we dissociated stimulus-response processing from feedback processing using "jittered" intervals of time between response and feedback to examine how each recruits the four primary corticostriatal loops (motor, executive, visual, and motivational). Second, we examined dynamic interactions within and between corticostriatal loops using Granger causality mapping. On each trial, subjects viewed one of six abstract visual stimuli, pressed a button indicating category membership, and then received feedback as to whether the decision was right or wrong. Stimulus-response processing was associated with greater activity in the visual loop, whereas feedback processing resulted in activity in the executive loop that was sensitive to feedback valence. Granger causality mapping showed patterns of directed influence within corticostriatal loops and between loops from the motor to the executive, to the visual, and finally to the motivational loop. These patterns of interaction are consistent with functional integration of motor processing in the motor loop with feedback processing in the executive loop and maintenance of stimulus-response history for future responses in the motivational loop.

Dissociating hippocampal and basal ganglia contributions to category learning using stimulus novelty and subjective judgments.

We identified factors leading to hippocampal and basal ganglia recruitment during categorization learning. Subjects alternated between blocks of a standard trial and error category learning task and a subjective judgment task. In the subjective judgments task subjects categorized the stimulus and then instead of receiving feedback they indicated the basis of their response using 4 options: Remember: Conscious episodic memory of previous trials. Know-Automatic: Automatic, rapid response accompanied by conscious awareness of category membership. Know-Intuition: A "gut feeling" without fully conscious knowledge of category membership. Guess: Guessing. In addition, new stimuli were introduced throughout the experiment to examine effects of novelty. Categorization overall recruited both the basal ganglia and posterior hippocampus. However, basal ganglia activity was found during Know judgments (both Automatic and Intuition), whereas posterior hippocampus activity was found during Remember judgments. Granger causality mapping indicated interactions between the basal ganglia and hippocampus, with the putamen exerting directed influence on the posterior hippocampus, which in turn exerted directed influence on the posterior caudate nucleus. We also found a region of anterior hippocampus that showed decreased activity relative to baseline during categorization overall, and showed a strong novelty effect. Our results indicate that subjective measures may be effective in dissociating basal ganglia from hippocampal dependent learning, and that the basal ganglia are involved in both conscious and unconscious learning. They also indicate a dissociation within the hippocampus, in which the anterior regions are sensitive to novelty, and the posterior regions are involved in memory based categorization learning.

Dissociating the contributions of independent corticostriatal systems to visual categorization learning through the use of reinforcement learning modeling and Granger causality modeling.

We dissociated the contributions to learning of four corticostriatal "loops" (interacting striatal and cortical regions): motor (putamen and motor cortex), visual (posterior caudate and visual cortex), executive (anterior caudate and prefrontal cortex), and motivational (ventral striatum and ventromedial frontal cortex). Subjects learned to categorize individual repeated images into one of two arbitrary categories via trial and error. We identified (1) regions sensitive to correct categorization, categorization learning, and feedback valence; (2) regions sensitive to prediction error (violation of feedback expectancy) and reward prediction (expected feedback associated with category response) using reinforcement learning modeling; and (3) directed influences between regions using Granger causality modeling. Each loop showed a unique pattern of sensitivity to each of these factors. Both the motor and visual loops were involved in acquisition of categorization ability: activity during correct categorization increased across learning and was sensitive to reward prediction. However, the posterior caudate received directed influence from visual cortex, whereas the putamen exerted directed influence on motor cortex. The motivational and executive loops were involved in feedback processing: both regions were sensitive to feedback valence, which interacted with learning across scans. However, the motivational loop activity reflected prediction error, whereas executive loop activity reflected reward prediction, consistent with the executive loop role in integrating reward and action. Granger causality modeling found directed influences between striatal and cortical regions within each loop. Across loops, the motor loop exerted directed influence on the executive loop which is consistent with the role of the executive loop in integrating feedback with stimulus-response history.

Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.

It is widely accepted that the striatum of the basal ganglia is a primary substrate for the learning and performance of skills. We provide evidence that two regions of the rat striatum, ventral and dorsal, play distinct roles in instrumental conditioning (skill learning), with the ventral striatum being critical for learning and the dorsal striatum being important for performance but, notably, not for learning. This implies an actor (dorsal) versus director (ventral) division of labor, which is a new variant of the widely discussed actor-critic architecture. Our results also imply that the successful performance of a skill can ultimately result in its establishment as a habit outside the basal ganglia.