Metabolic Diaschisis in Mild Traumatic Brain Injury.

Neurophysiological diaschisis presents in traumatic brain injury (TBI) as functional impairment distant to the lesion site caused by axonal neuroexcitation and deafferentation. Diaschisis studies in TBI models have evaluated acute phase functional and microstructural changes. Here, in vivo biochemical changes and cerebral blood flow (CBF) dynamics following TBI are studied with magnetic resonance. Behavioral assessments, magnetic resonance spectroscopy (MRS), and CBF measurements on rats followed cortical impact TBI. Data were acquired pre-TBI and 1-3 h, 2-days, 7-days, and 14-days post-TBI. MRS was performed on the ipsilateral and contralateral sides in the cortex, striatum, and thalamus. Metabolites measured by MRS included N-acetyl aspartate (NAA), aspartate (Asp), lactate (Lac), glutathione (GSH), and glutamate (Glu). Lesion volume expanded for 2 days post-TBI and then decreased. Ipsilateral CBF dropped acutely versus baseline on both sides (-62% ipsilateral, -48% contralateral, p < 0.05) but then recovered in cortex, with similar changes in ipsilateral striatum. Metabolic changes versus baseline included increased Asp (+640% by Day 7 post-TBI, p < 0.05) and Lac (+140% on Day 2 post-TBI, p < 0.05) in ipsilateral cortex, while GSH (-67% acutely, p < 0.05) and NAA decreased (-50% on Day 2, p < 0.05). In contralateral cortex Lac decreased (-73% acutely, p < 0.05). Analysis of variance showed significance for Side (p < 0.05), Time after TBI (p < 0.05), and interactions (p < 0.005) for Asp, GSH, Lac, and NAA. Transient decreases of GSH (-30%, p < 0.05, acutely) and NAA (-23% on Day 2, p < 0.05) occurred in ipsilateral striatum with reduced GSH (-42%, p < 0.005, acutely) in the contralateral striatum. GSH was decreased in ipsilateral thalamus (-59% ipsilateral on Day 2, p < 0.05). Delayed increases of total choline were seen in the contralateral thalamus were noted as well (+21% on Day 7 post-TBI, p < 0.05). Both CBF and neurometabolite concentration changes occurred remotely from the TBI site, both ipsilaterally and contralaterally. Decreased Lac levels on the contralateral cortex following TBI may be indicative of reduced anaerobic metabolism during the acute phase. The timing and locations of the changes suggest excitatory and inhibitory signaling processes are affecting post-TBI metabolic fluctuations.

Thermal Nickel-Catalyzed N-Arylation of NH-Sulfoximines with (Hetero)aryl Chlorides Enabled by PhPAd-DalPhos Ligation.

We report a versatile method for cross-coupling of NH-sulfoximines with (hetero)aryl chlorides, as well as bromide and sulfonate electrophiles, that makes use of the air-stable, commercial precatalyst (PhPAd-DalPhos)Ni(o-tol)Cl. Under optimized conditions a diverse electrophile scope is established, including the N-arylation of the pharmaceutical Clozapine. While 5 mol % Ni and 80 °C are commonly employed in this chemistry, successful examples utilizing 1 mol % Ni and/or 25 °C are presented. Competition experiments establish the superiority of NH-sulfoximine over primary sulfonamide as nucleophiles under these conditions.

Long-Term Neurocognitive Outcomes in Pediatric Nonfatal Drowning: Results of a Family Caregiver Survey.

BACKGROUND: Drowning is a leading cause of brain injury in children. Long-term outcome data for drowning survivors are sparse. This study reports neurocognitive outcomes for 154 children hospitalized following drowning. METHODS: A survey for parent caregivers was distributed online. Likert scale items assessed 10 outcome variables in four domains: motor (three), perception (three), language (three), and social/emotional (one). Cluster analysis, outcome relative risk, and descriptive statistics were applied. RESULTS: Of 208 surveys received, 154 met inclusion criteria. Coma was the most common admission status (n = 137). Cluster analysis identified three outcome groups: Mild (n = 39), Moderate (n = 75), and Severe (n = 40). Motor impairment with cognitive and perceptual sparing (deefferentation) was present in Moderate (P < 1 × 10-26) and Severe (P < 1 × 10-12) but absent in Mild. Locked-in state was endorsed in both Moderate (83%) and Severe (70%). The strongest predictor of good outcome (Mild) was hospitalization with no medical intervention (relative risk [RR] = 6.7). Responsivity on admission (RR = 4.2) or discharge (RR = 12.22) also predicted good outcome. In-hospital prognostication and counseling predicted outcome weakly (RR = 1.3) or not at all. CONCLUSIONS: Long-term outcomes in pediatric drowning ranged widely. Overall, motor impairments exceeded perceptual or cognitive (P < 1 × 10-18), with "locked-in state" endorsed in most (93 of 154). The strongest predictors of good outcome were the lack of necessity for interventions and responsivity on admission or discharge. The eponym "Conrad syndrome" is proposed for locked-in state following nonfatal drowning in children.

A randomized placebo-controlled trial of nicotinamide riboside in older adults with mild cognitive impairment.

Nicotinamide riboside (NR) increases blood levels of NAD+, a cofactor central to energy metabolism, and improves brain function in some rodent models of neurodegeneration. We conducted a placebo-controlled randomized pilot study with the primary objective of determining safety of NR in older adults with mild cognitive impairment (MCI). Twenty subjects with MCI were randomized to receive placebo or NR using dose escalation to achieve, and maintain, a final dose of 1 g/day over a 10-week study duration. The primary outcome was post-treatment change from baseline measures of cognition (Montreal Cognitive Assessment, MoCA). Predefined secondary outcomes included post-treatment changes in cerebral blood flow (CBF); blood NAD+ levels; and additional neurocognitive, psychometric, and physical performance tests. DNA methylation was assessed in peripheral blood mononuclear cells (PBMCs) as an exploratory outcome. The target NR dose was safely achieved as evidenced by a 2.6-fold increase in blood NAD+ in the NR group (p < 0.001, 95% CI [17.77, 43.49]) with no between-group difference in adverse event reporting. MoCA and other neurocognitive and psychometric metrics remained stable throughout the study. NR reduced CBF in the default mode network (DMN) with greatest differences observed in the left inferior parietal lobe (IPL) (DMN p = 0.013, µ = 0.92, 95% CI [0.23, 1.62]; left IPL p = 0.009, µ = 1.66, 95% CI [0.5, 2.82]). Walking speed in the placebo group significantly improved across the study duration suggestive of a practice effect but did not change in the NR group (p = 0.0402 and p = 0.4698, respectively). Other secondary outcome measures remained stable. Global methylation analyses indicated a modest NR-associated increase in DNA methylation and concomitant reduction in epigenetic age as measured by PhenoAge and GrimAge epigenetic clock analyses. In summary, NR significantly increased blood NAD+ concentrations in older adults with MCI. NR was well tolerated and did not alter cognition. While CBF was reduced by NR treatment, statistical significance would not have withstood multiple comparisons correction. A larger trial of longer duration is needed to determine the potential of NR as a strategy to improve cognition and alter CBF in older adults with MCI. ClinicalTrials.gov NCT02942888.

Riemannian frameworks for the harmonization of resting-state functional MRI scans.

Magnetic Resonance Imaging provides unprecedented images of the brain. Unfortunately, scanners and acquisition protocols can significantly impact MRI scans. The development of statistical methods able to reduce this variability without altering the relevant information in the scans, often coined harmonization methods, has been the topic of an increasing research effort supported by the recent growth of publicly available neuroimaging data sets and new possibilities for combining them to achieve greater statistical power. In this work, we focus on the challenges specifically raised by the harmonization of resting-state functional MRI scans. We propose to harmonize resting-state fMRI scans by reducing the impact of covariates such as scanner differences and scanning protocols on their associated functional connectomes and then propagating the changes back to the rs-fMRI time series. We use Riemannian geometric frameworks to preserve the mathematical properties of functional connectomes during their harmonization, and we demonstrate how state-of-the-art harmonization methods can be embedded within these frameworks to reduce covariates effects while preserving the relevant clinical information associated with aging or brain disorders. During our experiments, a large set of synthetic data was generated and processed to compare eighty variants of the proposed approach. The framework achieving the best harmonization was then applied to three low-dimensional data sets made of 712 sets of fMRI time series provided by the ABIDE consortium and two high-dimensional data sets obtained by processing 1527 rs-fMRI scans provided by the Human Connectome Project, the Framingham Heart Study and the Genetics of Brain Structure and Function study. These experiments established that our new framework could successfully harmonize low-dimensional connectomes and voxelwise functional time series and confirmed the need for preserving connectomes properties during their harmonization.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis.

Background: Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems. Methods: Through a comprehensive search in PubMed and Scopus databases, we reviewed 5,258 abstracts and identified 37 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of consistent antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation (TMS) therapy. Last, we studied the association of regional and network-level convergence maps with the selected neurotransmitter receptors/transporters maps. Results: We found regional convergence of the reported treatment-associated increases of functional measures in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. No regional convergence was found across foci of alterations in functional imaging associated with antidepressants. Moreover, we found network-level convergence of functional alterations in a circuit that was prominent in the frontoparietal and salience networks. This circuit was co-aligned with a circuit targeted by anti-subgenual TMS therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters. Conclusion: Our findings highlight the importance of the left dorsolateral prefrontal cortex, as well as frontoparietal network and the salience network in the therapeutic effects of anti-depressants, possibly associated with their role in improving executive functions and emotional processing.

Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository.

BACKGROUND: Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. OBJECTIVE: The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. METHODS: Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. RESULTS: A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. CONCLUSION: This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.

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.

Convergent abnormality in the subgenual anterior cingulate cortex in insomnia disorder: A revisited neuroimaging meta-analysis of 39 studies.

The neurobiological underpinnings of insomnia disorder (ID) are still poorly understood. A previous meta-analysis conducted by our research group in 2018 revealed no consistent regional alterations based on the limited number of eligible studies. Given the number of studies published during the last few years, we revisited the meta-analysis to provide an update to the field. Following the best-practice guidelines for conducting neuroimaging meta-analyses, we searched several databases (PubMed, Web of Science, and BrainMap) and identified 39 eligible structural and functional studies, reporting coordinates reflecting significant group differences between ID patients and healthy controls. A significant convergent regional alteration in the subgenual anterior cingulate cortex (sgACC) was observed using the activation likelihood estimation algorithm. Behavioural decoding using the BrainMap database indicated that this region is involved in fear-related emotional and cognitive processing. The sgACC showed robust task-based co-activation in meta-analytic connectivity modelling and task-free functional connectivity in a resting-state functional connectivity analysis with the main hubs of the salience and default mode networks, including the posterior cingulate cortex and dorsal ACC, amygdala, hippocampus, and medial prefrontal cortex. Collectively, the findings from this large-scale meta-analysis suggest a critical role of the sgACC in the pathophysiology of ID.

DSM-5 criterion-a-based trauma types in service members and veterans seeking treatment for posttraumatic stress disorder.

OBJECTIVE: In posttraumatic stress disorder (PTSD), the assumption of the equipotentiality of traumas ignores potentially unique contexts and consequences of different traumas. Accordingly, Stein et al. (2012) developed a reliable typing scheme in which assessors categorized descriptions of traumatic events into six "types": life threat to self (LTS), life threat to other, aftermath of violence (AV), traumatic loss, moral injury by self (MIS), and moral injury by other (MIO). We extended this research by validating the typing scheme using participant endorsements of type, rather than assesor-based types. We examined the concordance of participant and assesor types, frequency, and validity of participant-based trauma types by examining associations with baseline mental and behavioral health problems. METHOD: Interviewers enrolled military personnel and veterans (N = 1,443) in clinical trials of PTSD and helped them select the most currently distressing Criterion-A trauma. Participants and, archivally, assessors typed the distressing aspect(s) of this experience. RESULTS: AV was the most frequently participant-endorsed type, but LTS was the most frequently rated worst part of an event. Although participants endorsed MIS and MIO the least frequently, these were associated with worse mental and behavioral health problems. The agreement between participants and assessors regarding the worst part of the event was poor. CONCLUSION: Because of discrepancies between participant and assessor typologies, clinical researchers should use participants' ratings, and these should trump assessor judgment. Differences in pretreatment behavioral and mental health problems across some participant-endorsed trauma types partially support the validity of the participant ratings. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting new mineral occurrences and planetary analog environments via mineral association analysis.

The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent "messiness" of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation-hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time.

Interactions between Australian cancer physicians and the pharmaceutical industry: a qualitative study.

OBJECTIVES: To understand how and why Australian cancer physicians interact with the pharmaceutical industry. DESIGN: Qualitative study using semistructured interviews, performed by a medical oncologist. Thematic analysis using a combination of deductive and inductive codes. SETTING: Given the evidence on industry influences on clinical practice and the importance to the market of oncology drugs, we sought to better understand cancer physicians' experiences. Practising consultant medical oncologists and clinical haematologists from four Australian states were interviewed over Zoom. PARTICIPANTS: 16 cancer physicians were interviewed between November 2021 and March 2022, from 37 invited (response rate 43%). Most were medical oncologists (n=12 of 16, 75%) and male (n=9 of 16, 56%). OUTCOME MEASURES: The analysis of all interviews was based on grounded theory. Transcripts were coded and then codes formed into themes with supporting quotes. The themes were then placed into categories, used to describe the broad areas into which the themes could be grouped. RESULTS: Six themes were identified that fell within two broad categories: cancer physicians' views and experiences of interactions and management of these interactions. Views and experiences included: the transactional nature of relationships, risks of research dependence, ethical challenges and varied attitudes based on interaction type. Management themes included: lack of useful guidance and reduced interactions during the COVID-19 pandemic. These led to an overarching seventh theme, on the desire for a 'middle road'. Cancer physicians identified the transactional nature of industry relationships and felt uncomfortable with several types of interactions, including those with sales representatives. Most wanted less contact with industry, and the forced separation that occurred with the COVID-19 pandemic was generally welcome. CONCLUSIONS: Cancer physicians may have difficulty balancing the perceived need to interact with industry in modern cancer care while maintaining distance to minimise conflicts of interest. Further research is needed to assess management strategies in this area.

Cocktail-party listening and cognitive abilities show strong pleiotropy.

Introduction: The cocktail-party problem refers to the difficulty listeners face when trying to attend to relevant sounds that are mixed with irrelevant ones. Previous studies have shown that solving these problems relies on perceptual as well as cognitive processes. Previously, we showed that speech-reception thresholds (SRTs) on a cocktail-party listening task were influenced by genetic factors. Here, we estimated the degree to which these genetic factors overlapped with those influencing cognitive abilities. Methods: We measured SRTs and hearing thresholds (HTs) in 493 listeners, who ranged in age from 18 to 91 years old. The same individuals completed a cognitive test battery comprising 18 measures of various cognitive domains. Individuals belonged to large extended pedigrees, which allowed us to use variance component models to estimate the narrow-sense heritability of each trait, followed by phenotypic and genetic correlations between pairs of traits. Results: All traits were heritable. The phenotypic and genetic correlations between SRTs and HTs were modest, and only the phenotypic correlation was significant. By contrast, all genetic SRT-cognition correlations were strong and significantly different from 0. For some of these genetic correlations, the hypothesis of complete pleiotropy could not be rejected. Discussion: Overall, the results suggest that there was substantial genetic overlap between SRTs and a wide range of cognitive abilities, including abilities without a major auditory or verbal component. The findings highlight the important, yet sometimes overlooked, contribution of higher-order processes to solving the cocktail-party problem, raising an important caveat for future studies aiming to identify specific genetic factors that influence cocktail-party listening.

A Minimum Bayes Factor Based Threshold for Activation Likelihood Estimation.

Activation likelihood estimation (ALE) is among the most used algorithms to perform neuroimaging meta-analysis. Since its first implementation, several thresholding procedures had been proposed, all referred to the frequentist framework, returning a rejection criterion for the null hypothesis according to the critical p-value selected. However, this is not informative in terms of probabilities of the validity of the hypotheses. Here, we describe an innovative thresholding procedure based on the concept of minimum Bayes factor (mBF). The use of the Bayesian framework allows to consider different levels of probability, each of these being equally significant. In order to simplify the translation between the common ALE practice and the proposed approach, we analised six task-fMRI/VBM datasets and determined the mBF values equivalent to the currently recommended frequentist thresholds based on Family Wise Error (FWE). Sensitivity and robustness toward spurious findings were also analyzed. Results showed that the cutoff log10(mBF) = 5 is equivalent to the FWE threshold, often referred as voxel-level threshold, while the cutoff log10(mBF) = 2 is equivalent to the cluster-level FWE (c-FWE) threshold. However, only in the latter case voxels spatially far from the blobs of effect in the c-FWE ALE map survived. Therefore, when using the Bayesian thresholding the cutoff log10(mBF) = 5 should be preferred. However, being in the Bayesian framework, lower values are all equally significant, while suggesting weaker level of force for that hypothesis. Hence, results obtained through less conservative thresholds can be legitimately discussed without losing statistical rigor. The proposed technique adds therefore a powerful tool to the human-brain-mapping field.

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.

Psychometric Properties of the Self-Injurious Thoughts and Behaviors Interview-Short Form Among U.S. Active Duty Military Service Members and Veterans.

We assessed the interrater reliability, convergent validity, and discriminant validity of the Self-Injurious Thoughts and Behaviors Interview-Short Form (SITBI-SF) in a sample of 1,944 active duty service members and veterans seeking services for posttraumatic stress disorder (PTSD) and related conditions. The SITBI-SF demonstrated high interrater reliability and good convergent and discriminant validity. The measurement properties of the SITBI-SF were comparable across service members and veterans. Approximately 8% of participants who denied a history of suicidal ideation on the SITBI-SF reported suicidal ideation on a separate self-report questionnaire (i.e., discordant responders). Discordant responders reported significantly higher levels of PTSD symptoms than those who denied suicidal ideation on both response formats. Findings suggest that the SITBI-SF is a reliable and valid interview-based measure of suicide-related thoughts and behaviors for use with military service members and veterans. Suicide risk assessment might be optimized if the SITBI-SF interview is combined with a self-report measure of related constructs.

Meta-analytic connectivity modelling of functional magnetic resonance imaging studies in autism spectrum disorders.

Social and non-social deficits in autism spectrum disorders (ASD) persist into adulthood and may share common regions of aberrant neural activations. The current meta-analysis investigated activation differences between ASD and neurotypical controls irrespective of task type. Activation likelihood estimation meta-analyses were performed to examine consistent hypo-activated and/or hyper-activated regions for all tasks combined, and for social and non-social tasks separately; meta-analytic connectivity modelling and behavioral/paradigm analyses were performed to examine co-activated regions and associated behaviors. One hundred studies (mean age range = 18-41 years) were included. For all tasks combined, the ASD group showed significant (p < .05) hypo-activation in one cluster around the left amygdala (peak - 26, -2, -20, volume = 1336 mm3, maximum ALE = 0.0327), and this cluster co-activated with two other clusters around the right cerebellum (peak 42, -56, -22, volume = 2560mm3, maximum ALE = 0.049) Lobule VI/Crus I and the left fusiform gyrus (BA47) (peak - 42, -46, -18, volume = 1616 mm3, maximum ALE = 0.046) and left cerebellum (peak - 42, -58, -20, volume = 1616mm3, maximum ALE = 0.033) Lobule VI/Crus I. While the left amygdala was associated with negative emotion (fear) (z = 3.047), the left fusiform gyrus/cerebellum Lobule VI/Crus I cluster was associated with language semantics (z = 3.724) and action observation (z = 3.077). These findings highlight the left amygdala as a region consistently hypo-activated in ASD and suggest the potential involvement of fusiform gyrus and cerebellum in social cognition in ASD. Future research should further elucidate if and how amygdala-fusiform/cerebellar connectivity relates to social and non-social cognition in adults with ASD.

Co-alteration Network Architecture of Major Depressive Disorder: A Multi-modal Neuroimaging Assessment of Large-scale Disease Effects.

Major depressive disorder (MDD) exhibits diverse symptomology and neuroimaging studies report widespread disruption of key brain areas. Numerous theories underpinning the network degeneration hypothesis (NDH) posit that neuropsychiatric diseases selectively target brain areas via meaningful network mechanisms rather than as indistinct disease effects. The present study tests the hypothesis that MDD is a network-based disorder, both structurally and functionally. Coordinate-based meta-analysis and Activation Likelihood Estimation (CBMA-ALE) were used to assess the convergence of findings from 92 previously published studies in depression. An extension of CBMA-ALE was then used to generate a node-and-edge network model representing the co-alteration of brain areas impacted by MDD. Standardized measures of graph theoretical network architecture were assessed. Co-alteration patterns among the meta-analytic MDD nodes were then tested in independent, clinical T1-weighted structural magnetic resonance imaging (MRI) and resting-state functional (rs-fMRI) data. Differences in co-alteration profiles between MDD patients and healthy controls, as well as between controls and clinical subgroups of MDD patients, were assessed. A 65-node 144-edge co-alteration network model was derived for MDD. Testing of co-alteration profiles in replication data using the MDD nodes provided distinction between MDD and healthy controls in structural data. However, co-alteration profiles were not distinguished between patients and controls in rs-fMRI data. Improved distinction between patients and healthy controls was observed in clinically homogenous MDD subgroups in T1 data. MDD abnormalities demonstrated both structural and functional network architecture, though only structural networks exhibited between-groups differences. Our findings suggest improved utility of structural co-alteration networks for ongoing biomarker development.

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.