ALE meta-analyses of voxel-based morphometry studies: Parameter validation via large-scale simulations.

Activation likelihood estimation (ALE) meta-analysis has been applied to structural neuroimaging data since long, but up to now, any systematic assessment of the algorithm's behavior, power and sensitivity has been based on simulations using functional neuroimaging databases as their foundation. Here, we aimed to determine whether the guidelines offered by previous evaluations can be generalized to ALE meta-analyses of voxel-based morphometry (VBM) studies. We ran 365000 distinct ALE analyses filled with simulated experiments, randomly sampling parameters from BrainMap's VBM experiment database. We then examined the algorithm's sensitivity, its susceptibility to spurious convergence, and its susceptibility to excessive contributions by individual experiments. In general, the performance of the ALE algorithm was highly comparable between imaging modalities, with the algorithm's sensitivity and specificity reaching similar levels with structural data as previously observed with functional data. Because of the lower number of foci reported and the higher number of participants usually included in structural experiments, individual studies had, on average, a higher impact towards significant clusters. To prevent significant clusters from being driven by single experiments, we recommend that researchers include at least 23 experiments in a VBM ALE dataset, instead of the previously recommended minimum of n = 17. While these recommendations do not constitute hard borders, running ALE analyses on smaller datasets would require special diligence in assessing and reporting the contributions of experiments to individual clusters.

Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.

Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and healthy controls. Unfortunately, the subtle brain alterations captured by these models are difficult to interpret because of the complexity of these multi-layer and non-linear models. Several heatmap methods have been proposed to address this issue and analyze the imaging patterns extracted from the deep neural networks, but no quantitative comparison between these methods has been carried out so far. In this work, we explore these questions by deriving heatmaps from Convolutional Neural Networks (CNN) trained using T1 MRI scans of the ADNI data set and by comparing these heatmaps with brain maps corresponding to Support Vector Machine (SVM) activation patterns. Three prominent heatmap methods are studied: Layer-wise Relevance Propagation (LRP), Integrated Gradients (IG), and Guided Grad-CAM (GGC). Contrary to prior studies where the quality of heatmaps was visually or qualitatively assessed, we obtained precise quantitative measures by computing overlap with a ground-truth map from a large meta-analysis that combined 77 voxel-based morphometry (VBM) studies independently from ADNI. Our results indicate that all three heatmap methods were able to capture brain regions covering the meta-analysis map and achieved better results than SVM activation patterns. Among them, IG produced the heatmaps with the best overlap with the independent meta-analysis.

Pertuzumab study in the neoadjuvant setting for HER2-positive nonmetastatic breast cancer in Australia (PeRSIA).

The pertuzumab study in the neoadjuvant setting for HER2+ nonmetastatic breast cancer in Australia (PeRSIA-ML39622) is an analysis of safety and effectiveness data from the pertuzumab patient registry. Although the prognosis of patients with early stage HER2+ breast cancer has been greatly improved by advances in chemotherapy approximately 25% to 30% of patients develop recurrent disease. Our study aimed to examine the effectiveness of neoadjuvant pertuzumab on surgical outcomes, describe the medium-term effectiveness outcomes of patients treated with pertuzumab, and describe the planned and actual anticancer treatment regimens that patients received. Deidentified data were collected from the patients' medical records and entered into REDCap, between March 2018 and July 2019 (n = 95). The adverse events (AEs) reported most frequently were diarrhea (20; 21.1%), rash (4; 4.2%), and LVSD (4; 4.2%; two patients during neoadjuvant treatment and two patients during adjuvant treatment). AEs, ≥Grade 3 were diarrhea (2; 2.1%) and LVSD (1; 1.1%). Following surgery, a breast pathological complete response (bpCR) was achieved in 65 patients (70.7%; 95% CI: 60.2%-79.7%) and total pathological complete response (tpCR) in 59 patients (64.1%; 95% CI: 53.4%-73.9%). All patients who did not achieve a tpCR obtained a partial response (33/92, 35.9%). Our study is the first to capture real-world data on the use of pertuzumab in the neoadjuvant setting in Australia. The effectiveness and safety data are consistent with those reported in clinical trials of pertuzumab in patients with HER2+ breast cancer, with no new safety concerns.

Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data.

The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.

PET/CT targeted tissue sampling reveals virus specific dIgA can alter the distribution and localization of HIV after rectal exposure.

Human immunodeficiency virus (HIV) vaccines have not been successful in clinical trials. Dimeric IgA (dIgA) in the form of secretory IgA is the most abundant antibody class in mucosal tissues, making dIgA a prime candidate for potential HIV vaccines. We coupled Positron Emission Tomography (PET) imaging and fluorescent microscopy of 64Cu-labeled, photoactivatable-GFP HIV (PA-GFP-BaL) and fluorescently labeled dIgA to determine how dIgA antibodies influence virus interaction with mucosal barriers and viral penetration in colorectal tissue. Our results show that HIV virions rapidly disseminate throughout the colon two hours after exposure. The presence of dIgA resulted in an increase in virions and penetration depth in the transverse colon. Moreover, virions were found in the mesenteric lymph nodes two hours after viral exposure, and the presence of dIgA led to an increase in virions in mesenteric lymph nodes. Taken together, these technologies enable in vivo and in situ visualization of antibody-virus interactions and detailed investigations of early events in HIV infection.

Imaging local genetic influences on cortical folding.

Recent progress in deciphering mechanisms of human brain cortical folding leave unexplained whether spatially patterned genetic influences contribute to this folding. High-resolution in vivo brain MRI can be used to estimate genetic correlations (covariability due to shared genetic factors) in interregional cortical thickness, and biomechanical studies predict an influence of cortical thickness on folding patterns. However, progress has been hampered because shared genetic influences related to folding patterns likely operate at a scale that is much more local (<1 cm) than that addressed in prior imaging studies. Here, we develop methodological approaches to examine local genetic influences on cortical thickness and apply these methods to two large, independent samples. We find that such influences are markedly heterogeneous in strength, and in some cortical areas are notably stronger in specific orientations relative to gyri or sulci. The overall, phenotypic local correlation has a significant basis in shared genetic factors and is highly symmetric between left and right cortical hemispheres. Furthermore, the degree of local cortical folding relates systematically with the strength of local correlations, which tends to be higher in gyral crests and lower in sulcal fundi. The relationship between folding and local correlations is stronger in primary sensorimotor areas and weaker in association areas such as prefrontal cortex, consistent with reduced genetic constraints on the structural topology of association cortex. Collectively, our results suggest that patterned genetic influences on cortical thickness, measurable at the scale of in vivo MRI, may be a causal factor in the development of cortical folding.

Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults.

Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.

Meta-analysis in human neuroimaging: computational modeling of large-scale databases.

Spatial normalization--applying standardized coordinates as anatomical addresses within a reference space--was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.

Evaluation of thresholding methods for activation likelihood estimation meta-analysis via large-scale simulations.

In recent neuroimaging studies, threshold-free cluster enhancement (TFCE) gained popularity as a sophisticated thresholding method for statistical inference. It was shown to feature higher sensitivity than the frequently used approach of controlling the cluster-level family-wise error (cFWE) and it does not require setting a cluster-forming threshold at voxel level. Here, we examined the applicability of TFCE to a widely used method for coordinate-based neuroimaging meta-analysis, Activation Likelihood Estimation (ALE), by means of large-scale simulations. We created over 200,000 artificial meta-analysis datasets by independently varying the total number of experiments included and the amount of spatial convergence across experiments. Next, we applied ALE to all datasets and compared the performance of TFCE to both voxel-level and cluster-level FWE correction approaches. All three multiple-comparison correction methods yielded valid results, with only about 5% of the significant clusters being based on spurious convergence, which corresponds to the nominal level the methods were controlling for. On average, TFCE's sensitivity was comparable to that of cFWE correction, but it was slightly worse for a subset of parameter combinations, even after TFCE parameter optimization. cFWE yielded the largest significant clusters, closely followed by TFCE, while voxel-level FWE correction yielded substantially smaller clusters, showcasing its high spatial specificity. Given that TFCE does not outperform the standard cFWE correction but is computationally much more expensive, we conclude that employing TFCE for ALE cannot be recommended to the general user.

Immediate and long-term effects of speech treatment targets and intensive dosage on Parkinson's disease dysphonia and the speech motor network: Randomized controlled trial.

This study compared acoustic and neural changes accompanying two treatments matched for intensive dosage but having two different treatment targets (voice or articulation) to dissociate the effects of treatment target and intensive dosage in speech therapies. Nineteen participants with Parkinsonian dysphonia (11 F) were randomized to three groups: intensive treatment targeting voice (voice group, n = 6), targeting articulation (articulation group, n = 7), or an untreated group (no treatment, n = 6). The severity of dysphonia was assessed by the smoothed cepstral peak prominence (CPPS) and neuronal changes were evaluated by cerebral blood flow (CBF) recorded at baseline, posttreatment, and 7-month follow-up. Only the voice treatment resulted in significant posttreatment improvement in CPPS, which was maintained at 7 months. Following voice treatment, increased activity in left premotor and bilateral auditory cortices was observed at posttreatment, and in the left motor and auditory cortices at 7-month follow-up. Articulation treatment resulted in increased activity in bilateral premotor and left insular cortices that were sustained at a 7-month follow-up. Activation in the auditory cortices and a significant correlation between the CPPS and CBF in motor and auditory cortices was observed only in the voice group. The intensive dosage resulted in long-lasting behavioral and neural effects as the no-treatment group showed a progressive decrease in activity in areas of the speech motor network out to a 7-month follow-up. These results indicate that dysphonia and the speech motor network can be differentially modified by treatment targets, while intensive dosage contributes to long-lasting effects of speech treatments.

Single-molecule strong coupling at room temperature in plasmonic nanocavities.

Photon emitters placed in an optical cavity experience an environment that changes how they are coupled to the surrounding light field. In the weak-coupling regime, the extraction of light from the emitter is enhanced. But more profound effects emerge when single-emitter strong coupling occurs: mixed states are produced that are part light, part matter1, 2, forming building blocks for quantum information systems and for ultralow-power switches and lasers. Such cavity quantum electrodynamics has until now been the preserve of low temperatures and complicated fabrication methods, compromising its use. Here, by scaling the cavity volume to less than 40 cubic nanometres and using host­guest chemistry to align one to ten protectively isolated methylene-blue molecules, we reach the strong-coupling regime at room temperature and in ambient conditions. Dispersion curves from more than 50 such plasmonic nanocavities display characteristic light­matter mixing, with Rabi frequencies of 300 millielectronvolts for ten methylene-blue molecules, decreasing to 90 millielectronvolts for single molecules­matching quantitative models. Statistical analysis of vibrational spectroscopy time series and dark-field scattering spectra provides evidence of single-molecule strong coupling. This dressing of molecules with light can modify photochemistry, opening up the exploration of complex natural processes such as photosynthesis and the possibility of manipulating chemical bonds.

Using three scenarios to explain life expectancy in advanced cancer: attitudes of patients, family members, and other healthcare professionals.

AIM: To evaluate a web-based tool for estimating and explaining three scenarios for expected survival time to people with advanced cancer (patients), their family members (FMs), and other healthcare professionals (HCPs). METHODS: Thirty-three oncologists estimated the "median survival of a group of similar patients" for patients seeking quantitative prognostic information. The web-based tool generated worst-case, most likely, and best-case scenarios for survival based on the oncologist's estimate. Oncologists presented the scenarios to each patient and provided a printed summary to patients, FMs, and HCPs. Attitudes to the information were assessed by questionnaires. Observed survival for each patient was compared with the oncologist's estimated survival and the three scenarios. RESULTS: Prognosis was discussed with 222 patients: median age 67 years; 61% male; most common primary sites pancreas 15%, non-small-cell lung 15%, and colorectal 12%. The median (range) for observed survival times was 9 months (0.5-43) and for oncologist's estimated survival times was 12 months (2-96). Ninety-one percent of patients, 91% of FMs, and 84% of HCPs agreed that it was helpful having life expectancy explained as three scenarios. The majority (77%) of patients judged the information presented about their life expectancy to be the same or better than they had expected before the consultation. The survival estimates met a priori criteria for calibration, precision, and accuracy. CONCLUSIONS: Patients, FMs, and HCPs found it helpful to receive personalized prognostic information formatted as three scenarios for survival. It was feasible, acceptable, and safe to use a web-based resource to do this.

A meta-analysis on neural changes of cognitive training for mental disorders in executive function tasks: increase or decrease brain activation?

BACKGROUND: Cognitive impairment is often found in patients with psychiatric disorders, and cognitive training (CT) has been shown to help these patients. To better understand the mechanisms of CT, many neuroimaging studies have investigated the neural changes associated with it. However, the results of those studies have been inconsistent, making it difficult to draw conclusions from the literature. Therefore, the objective of this meta-analysis was to identify consistent patterns in the literature of neural changes associated with CT for psychiatric disorders. METHODS: We searched for cognitive training imaging studies in PubMed, Cochrane library, Scopus, and ProQuest electronic databases. We conducted an activation likelihood estimation (ALE) for coordinate-based meta-analysis of neuroimaging studies, conduct behavioral analysis of brain regions identified by ALE analysis, conduct behavioral analysis of brain regions identified by ALE analysis, and then created a functional meta-analytic connectivity model (fMACM) of the resulting regions. RESULTS: Results showed that CT studies consistently reported increased activation in the left inferior frontal gyrus (IFG) and decreased activation in the left precuneus and cuneus from pre- to post- CT. CONCLUSION: CT improves cognitive function by supporting language and memory function, and reducing neuronal resources associated with basic visual processing.

BACON: A tool for reverse inference in brain activation and alteration.

Over the past decades, powerful MRI-based methods have been developed, which yield both voxel-based maps of the brain activity and anatomical variation related to different conditions. With regard to functional or structural MRI data, forward inferences try to determine which areas are involved given a mental function or a brain disorder. A major drawback of forward inference is its lack of specificity, as it suggests the involvement of brain areas that are not specific for the process/condition under investigation. Therefore, a different approach is needed to determine to what extent a given pattern of cerebral activation or alteration is specifically associated with a mental function or brain pathology. In this study, we present a new tool called BACON (Bayes fACtor mOdeliNg) for performing reverse inference both with functional and structural neuroimaging data. BACON implements the Bayes' factor and uses the activation likelihood estimation derived-maps to obtain posterior probability distributions on the evidence of specificity with regard to a particular mental function or brain pathology.

Disruption of the Atrophy-based Functional Network in Multiple Sclerosis Is Associated with Clinical Disability: Validation of a Meta-Analytic Model in Resting-State Functional MRI.

Background In multiple sclerosis (MS), gray matter (GM) atrophy exhibits a specific pattern, which correlates strongly with clinical disability. However, the mechanism of regional specificity in GM atrophy remains largely unknown. Recently, the network degeneration hypothesis (NDH) was quantitatively defined (using coordinate-based meta-analysis) as the atrophy-based functional network (AFN) model, which posits that localized GM atrophy in MS is mediated by functional networks. Purpose To test the NDH in MS in a data-driven manner using the AFN model to direct analyses in an independent test sample. Materials and Methods Model fit testing was conducted with structural equation modeling, which is based on the computation of semipartial correlations. Model verification was performed in coordinate-based data of healthy control participants from the BrainMap database (https://www.brainmap.org). Model validation was conducted in prospectively acquired resting-state functional MRI in participants with relapsing-remitting MS who were recruited between September 2018 and January 2019. Correlation analyses of model fit indices and volumetric measures with Expanded Disability Status Scale (EDSS) scores and disease duration were performed. Results Model verification of healthy control participants included 80 194 coordinates from 9035 experiments. Model verification in healthy control data resulted in excellent model fit (root mean square error of approximation, 0.037; 90% CI: 0.036, 0.039). Twenty participants (mean age, 36 years ± 9 [standard deviation]; 12 women) with relapsing-remitting MS were evaluated. Model validation in resting-state functional MRI in participants with MS resulted in deviation from optimal model fit (root mean square error of approximation, 0.071; 90% CI: 0.070, 0.072), which correlated with EDSS scores (r = 0.68; P = .002). Conclusion The atrophy-based functional network model predicts functional network disruption in multiple sclerosis (MS), thereby supporting the network degeneration hypothesis. On resting-state functional MRI scans, reduced functional network integrity in participants with MS had a strong positive correlation with clinical disability. © RSNA, 2021 Online supplemental material is available for this article.

Validation of T2 -based oxygen extraction fraction measurement with 15 O positron emission tomography.

PURPOSE: To evaluate the accuracy of T2 -based whole-brain oxygen extraction fraction (OEF) estimation by comparing it with gold standard 15 O-PET measurements. METHODS: Sixteen healthy adult subjects underwent MRI and 15 O-PET OEF measurements on the same day. On MRI, whole-brain OEF was quantified by T2 -relaxation-under-spin-tagging (TRUST) MRI, based on subject-specific hematocrit. The TRUST OEF was compared to the whole-brain averaged OEF produced by 15 O-PET. Agreement between TRUST and 15 O-PET whole-brain OEF measurements was examined in terms of intraclass correlation coefficient (ICC) and in absolute OEF values. In a subset of 10 subjects, test-retest reproducibility of whole-brain OEF was also evaluated and compared between the two modalities. RESULTS: Across the 16 subjects, the mean whole-brain OEF of TRUST and 15 O-PET were 36.44 ± 4.07% and 36.45 ± 3.65%, respectively, showing no difference between the two modalities (P = .99). TRUST whole-brain OEF strongly correlated with that of 15 O-PET (N = 16, ICC = 0.90, P = 4 × 10-7 ). The coefficient-of-variation of TRUST and 15 O-PET whole-brain OEF measurements were 1.79 ± 0.67% and 2.06 ± 1.55%, respectively, showing no difference between the two modalities (N = 10, P = .64). Further analyses on the effect of hematocrit revealed that correlation between PET OEF and TRUST OEF with assumed hematocrit remained significant (ICC = 0.8, P < 2 × 10-5 ). CONCLUSION: Whole-brain OEF measured by TRUST was in excellent agreement with gold standard 15 O-PET, with highly comparable accuracy and reproducibility. These findings suggest that TRUST MRI can provide accurate quantification of whole-brain OEF noninvasively.

Restriction Spectrum Imaging-Magnetic Resonance Imaging to Improve Prostate Cancer Imaging in Men on Active Surveillance.

PURPOSE: Restriction spectrum imaging-magnetic resonance imaging is a short duration enhanced diffusion-weighted technique that seeks to standardize sequences and predict upgrading. We test this technology for active surveillance biopsies. Our objective is to investigate the utility of restriction spectrum imaging-magnetic resonance imaging to improve upgrading detection in a prostate cancer active surveillance cohort. MATERIALS AND METHODS: We prospectively enrolled men on active surveillance undergoing repeat biopsy from January 2016 to June 2019. Subjects underwent prostate multiparametric magnetic resonance imaging and restriction spectrum imaging-magnetic resonance imaging reviewed by a urological radiologist for PI-RADS® scored lesions, followed by magnetic resonance imaging-guided prostate biopsy by a urologist. Restriction spectrum imaging-magnetic resonance imaging analysis with proprietary research software (CorTechs Labs, San Diego, California) generated a restricted signal map. We compared the restricted signal map and apparent diffusion coefficient values using T-test, ANOVA, and logistic regression analyses for prediction of upgrading. RESULTS: Of 123 enrolled men we identified 74 restriction spectrum imaging-magnetic resonance imaging regions of interest (targeted lesions) in 110 subjects, with 105 subjects completing biopsy. The restricted signal map was significant per PI-RADS score for true-positive lesion detection (mean difference 28, SD 0.7, p=0.001), and better than apparent diffusion coefficient (mean difference -15, SD 55, p=0.6). Restriction spectrum imaging generated restricted signal map values >50 improved sensitivity, specificity, positive predictive value and negative predictive value (81.0%, 81.8%, 54.2% and 94.2%) over PI-RADS ≥3 (71.4%, 38.9%, 23.7% and 83.7%, respectively) for Gleason upgrading. Overall restriction spectrum imaging is able to improve the AUC of 0.70 (95% CI 0.49-0.92, p=0.03) to 0.90 (95% CI 0.82-0.98, p <0.001). CONCLUSIONS: Restriction spectrum imaging-magnetic resonance imaging enhances the standard PI-RADS system by providing a noninvasive radiological biomarker to predict upgrading in active surveillance.

Using the Schmahmann Syndrome Scale to Assess Cognitive Impairment in Young Adults with Metabolic Syndrome: a Hypothesis-Generating Report.

The posterior cerebellum is the most significantly compromised brain structure in individuals with metabolic syndrome (MetS) (Hum Brain Mapp 40(12):3575-3588, 2019). In light of this, we hypothesized that cognitive decline reported in patients with MetS is likely related to posterior cerebellar atrophy. In this study, we performed a post hoc analyses using T1-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) in the form of voxel-wise tract-based spatial statistics (TBSS), biometric, and psychometric data from young participants with (n = 52, aged 18-35 years) and without MetS (n = 52, aged 18-35 years). To test the predictive value of components of the Schmahmann syndrome scale (SSS), also known as the cerebellar cognitive affective syndrome scale, we used structural equation modeling to adapt available psychometric scores in our participant sample to the SSS and compare them to the composite score of all psychometric data available. Our key findings point to a statistically significant correlation between TBSS fractional anisotropy (FA) values from DTI and adapted SSS psychometric scores in individuals with MetS (r2 = .139, 95% CI = 0.009, .345). This suggests that the SSS could be applied to assess cognitive and likely neuroanatomical effects associated with MetS. We strongly suggest that future work aimed at investigating the neurocognitive effects of MetS and related comorbidities (i.e., dyslipidemia, diabetes, obesity) would benefit from implementing and further exploring the validity of the SSS in this patient population.

Minimal Relationship between Local Gyrification and General Cognitive Ability in Humans.

Previous studies suggest that gyrification is associated with superior cognitive abilities in humans, but the strength of this relationship remains unclear. Here, in two samples of related individuals (total N = 2882), we calculated an index of local gyrification (LGI) at thousands of cortical surface points using structural brain images and an index of general cognitive ability (g) using performance on cognitive tests. Replicating previous studies, we found that phenotypic and genetic LGI-g correlations were positive and statistically significant in many cortical regions. However, all LGI-g correlations in both samples were extremely weak, regardless of whether they were significant or nonsignificant. For example, the median phenotypic LGI-g correlation was 0.05 in one sample and 0.10 in the other. These correlations were even weaker after adjusting for confounding neuroanatomical variables (intracranial volume and local cortical surface area). Furthermore, when all LGIs were considered together, at least 89% of the phenotypic variance of g remained unaccounted for. We conclude that the association between LGI and g is too weak to have profound implications for our understanding of the neurobiology of intelligence. This study highlights potential issues when focusing heavily on statistical significance rather than effect sizes in large-scale observational neuroimaging studies.

Quantifying ecological impacts of mass extinctions with network analysis of fossil communities.

Mass extinctions documented by the fossil record provide critical benchmarks for assessing changes through time in biodiversity and ecology. Efforts to compare biotic crises of the past and present, however, encounter difficulty because taxonomic and ecological changes are decoupled, and although various metrics exist for describing taxonomic turnover, no methods have yet been proposed to quantify the ecological impacts of extinction events. To address this issue, we apply a network-based approach to exploring the evolution of marine animal communities over the Phanerozoic Eon. Network analysis of fossil co-occurrence data enables us to identify nonrandom associations of interrelated paleocommunities. These associations, or evolutionary paleocommunities, dominated total diversity during successive intervals of relative community stasis. Community turnover occurred largely during mass extinctions and radiations, when ecological reorganization resulted in the decline of one association and the rise of another. Altogether, we identify five evolutionary paleocommunities at the generic and familial levels in addition to three ordinal associations that correspond to Sepkoski's Cambrian, Paleozoic, and Modern evolutionary faunas. In this context, we quantify magnitudes of ecological change by measuring shifts in the representation of evolutionary paleocommunities over geologic time. Our work shows that the Great Ordovician Biodiversification Event had the largest effect on ecology, followed in descending order by the Permian-Triassic, Cretaceous-Paleogene, Devonian, and Triassic-Jurassic mass extinctions. Despite its taxonomic severity, the Ordovician extinction did not strongly affect co-occurrences of taxa, affirming its limited ecological impact. Network paleoecology offers promising approaches to exploring ecological consequences of extinctions and radiations.