Disentangling the role of gray matter volume and concentration in autism spectrum disorder: A meta-analytic investigation of 25 years of voxel-based morphometry research.

Despite over two decades of neuroimaging research, a unanimous definition of the pattern of structural variation associated with autism spectrum disorder (ASD) has yet to be found. One potential impeding issue could be the sometimes ambiguous use of measurements of variations in gray matter volume (GMV) or gray matter concentration (GMC). In fact, while both can be calculated using voxel-based morphometry analysis, these may reflect different underlying pathological mechanisms. We conducted a coordinate-based meta-analysis, keeping apart GMV and GMC studies of subjects with ASD. Results showed distinct and non-overlapping patterns for the two measures. GMV decreases were evident in the cerebellum, while GMC decreases were mainly found in the temporal and frontal regions. GMV increases were found in the parietal, temporal, and frontal brain regions, while GMC increases were observed in the anterior cingulate cortex and middle frontal gyrus. Age-stratified analyses suggested that such variations are dynamic across the ASD lifespan. The present findings emphasize the importance of considering GMV and GMC as distinct yet synergistic indices in autism research.

Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research.

Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.

Donanemab, another anti-Alzheimer's drug with risk and uncertain benefit.

Based on "reducing amyloid plaques in the brain", the U.S. Food and Drug Administration has granted accelerated and full approval for two monoclonal anti-Alzheimer's antibodies, aducanumab and lecanemab, respectively. Approval of a third antibody, donanemab, is pending. Moreover, lecanemab and donanemab are claimed to cause delay in the cognitive decline that characterizes the disease. We believe that these findings are subject to misinterpretation and statistical bias. Donanemab is claimed to cause removal of up to 86 % of cerebral amyloid and 36 % delay in cognitive decline compared to placebo. In reality, these are very small changes on an absolute scale and arguably less than what can be achieved with cholinesterase inhibitor/memantine therapy. Moreover, the "removal" of amyloid, based on the reduced accumulation of amyloid-PET tracer, most likely also reflects therapy-related tissue damage. This would also correlate with the minimal clinical effect, the increased frequency of amyloid-related imaging abnormalities, and the accelerated loss of brain volume in treated compared to placebo patients observed with these antibodies. We recommend halting approvals of anti-AD antibodies until these issues are fully understood to ensure that antibody treatment does not cause more harm than benefit to patients.

Building diagnostic neuroimaging biomarkers for psychiatric disorders using reverse inference approaches: A viable route?

The advent of structural magnetic resonance imaging (sMRI) at the end of the 20th century opened the way toward a deeper understanding of the neurophysiology of psychiatric disorders, substantiating regional structural abnormalities underlying this group of clinical conditions. However, despite abundant and flourishing scientific research, sMRI methodologies are not currently integrated into daily diagnostic practice. One reason behind this failed translation may be the prevailing approach to logical reasoning in neuroimaging: The forward inference via frequentist-based statistics. This reasoning prevents clinicians from obtaining information about the selectivity of results, which are therefore of limited use regarding the definition of biomarkers and refinement of diagnostic processes. Recently, another type of inferential approach has started to emerge in the neuroimaging field: The reverse inference via Bayesian statistics. Here, we introduce the key concepts of this approach, with a particular emphasis on the clinical sMRI environment. We survey recent findings showing significant potential for clinical translation. Clinical opportunities and challenges for developing reverse inference-based neural markers for psychiatry are also discussed. We propose that a systematic sharing of imaging data across the human brain mapping community is an essential first step toward a paradigmatic clinical shift. We conclude that a defined synergy between forward-based and reverse-based sMRI research can illuminate current discussions on diagnostic brain markers, offering clarity on key issues and fostering new tailored diagnostic avenues.

The Functional Neuroimaging of Autobiographical Memory for Happy Events: A Coordinate-Based Meta-Analysis.

Neuroimaging studies using autobiographical recall methods investigated the neural correlates of happy autobiographical memories (AMs). The scope of the present activation likelihood estimation (ALE) meta-analysis was to quantitatively analyze neuroimaging studies of happy AMs conducted with autobiographical recall paradigms. A total of 17 studies (12 fMRI; 5 PET) on healthy individuals were included in this meta-analysis. During recall of happy life events, consistent activation foci were found in the frontal gyrus, the cingulate cortex, the basal ganglia, the parahippocampus/hippocampus, the hypothalamus, and the thalamus. The result of this quantitative coordinate-based ALE meta-analysis provides an objective view of brain responses associated with AM recollection of happy events, thus identifying brain areas consistently activated across studies. This extended brain network included frontal and limbic regions involved in remembering emotionally relevant positive events. The frontal gyrus and the cingulate cortex may be responsible for cognitive appraisal processes during recollection of happy AMs, while the subthalamic nucleus and globus pallidus may be involved in pleasure reactions associated with recollection of happy life events. These findings shed light on the neural network involved in recalling positive AMs in healthy individuals, opening further avenues for future research in clinical populations with mood disorders.

Why classical receptor theory, which ignores allostery, can effectively measure the strength of an allosteric effect as agonist's efficacy.

BACKGROUND AND PURPOSE: The classical theory of receptor action has been used for decades as a powerful tool to estimate molecular determinants of ligand-induced receptor activation (i.e., affinity and efficacy) from experimentally observable biological responses. However, it is also a well-recognized fact that the receptor-binding and activation mechanisms, and the parameters thereof, described in the classical theory contradict with the modern view of receptor activation based on allosteric principles. EXPERIMENTAL APPROACH: We used mathematical analysis, along with some numerical simulations, to answer the key question as to what extent the classical theory is compatible-if at all-with the modern understanding of receptor activation. KEY RESULTS: Here, we showed conclusively that (1) receptor activation equations based on allosteric principles contain the logic of the classical theory in disguise, and therefore, (2) estimates of "intrinsic efficacy" (ε) obtained by means of classical techniques (i.e., null methods or fitting the operational model to concentration-response data) are equivalent to the allosteric coupling factors that represent the molecular efficacy of ligands. CONCLUSION AND IMPLICATIONS: Thus, we conclude that despite the justified criticisms it has received so far, the classical theory may continue to be useful in estimating ligand efficacy from experimental data, if used properly. Here, we also provide rigorous criteria for the proper use of the theory. These findings not only have implications for ligand classification but also resolve some long lasting discussions in the field of bias agonism in GPCR, which requires reasonable estimates of relative ligand efficacies at different signalling pathways.

Atypical local brain connectivity in pediatric autism spectrum disorder? A coordinate-based meta-analysis of regional homogeneity studies.

Despite decades of massive neuroimaging research, the comprehensive characterization of short-range functional connectivity in autism spectrum disorder (ASD) remains a major challenge for scientific advances and clinical translation. From the theoretical point of view, it has been suggested a generalized local over-connectivity that would characterize ASD. This stance is known as the general local over-connectivity theory. However, there is little empirical evidence supporting such hypothesis, especially with regard to pediatric individuals with ASD (age [Formula: see text] 18 years old). To explore this issue, we performed a coordinate-based meta-analysis of regional homogeneity studies to identify significant changes of local connectivity. Our analyses revealed local functional under-connectivity patterns in the bilateral posterior cingulate cortex and superior frontal gyrus (key components of the default mode network) and in the bilateral paracentral lobule (a part of the sensorimotor network). We also performed a functional association analysis of the identified areas, whose dysfunction is clinically consistent with the well-known deficits affecting individuals with ASD. Importantly, we did not find relevant clusters of local hyper-connectivity, which is contrary to the hypothesis that ASD may be characterized by generalized local over-connectivity. If confirmed, our result will provide a valuable insight into the understanding of the complex ASD pathophysiology.

Revision of Alzheimer's diagnostic criteria or relocation of the Potemkin village.

The recently announced revision of the Alzheimer's disease (AD) diagnostic ATN classification adds to an already existing disregard for clinical assessment the rejection of image-based in vivo assessment of the brain's condition. The revision suggests that the diagnosis of AD should be based solely on the presence of cerebral amyloid-beta and tau, indicated by the "A" and "T". The "N", which stands for neurodegeneration - detected by imaging - should no longer be given importance, except that A+ ± T + = AD with amyloid PET being the main method for demonstrating A+ . We believe this is an artificial and misleading suggestion. It is artificial because it relies on biomarkers whose significance remains obscure and where the detection of "A" is based on a never-validated PET method using a tracer that marks much more than amyloid-beta. It is misleading because many patients without dementia will be falsely classified as having AD, but nonetheless candidates for passive immunotherapy, which may be more harmful than beneficial, and sometimes fatal.

Unleashing the Power of Bayesian Re-Analysis: Enhancing Insights into Lecanemab (Clarity AD) Phase III Trial Through Informed t-Test.

BACKGROUND: Clinical trials targeting Alzheimer's disease (AD) aim to alleviate clinical symptoms and alter the course of this complex neurodegenerative disorder. However, the conventional approach of null hypothesis significance testing (NHST) commonly employed in such trials has inherent limitations in assessing clinical significance and capturing nuanced evidence of effectiveness on a continuous scale. OBJECTIVE: In this study, we conducted a re-analysis of the phase III trial of lecanemab, a recently proposed humanized IgG1 monoclonal antibody with high affinity for Aß soluble protofibrils, using a Bayesian approach with informed t-test priors. METHODS: To achieve this, we carefully selected trial data and derived effect size estimates for the primary endpoint, the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). Subsequently, a series of Bayes Factor analyses were performed to compare evidence supporting the null hypothesis (no treatment effect) versus the alternative hypothesis (presence of an effect). Drawing on relevant literature and the lecanemab phase III trial, we incorporated different minimal clinically important difference (MCID) values for the primary endpoint CDR-SB as prior information. RESULTS: Our findings, based on a standard prior, revealed anecdotal evidence favoring the null hypothesis. Additional robustness checks yielded consistent results. However, when employing informed priors, we observed varying evidence across different MCID values, ultimately indicating no support for the effectiveness of lecanemab over placebo. CONCLUSION: Our study underscores the value of Bayesian analysis in clinical trials while emphasizing the importance of incorporating MCID and effect size granularity to accurately assess treatment efficacy.

CBMAT: a MATLAB toolbox for data preparation and post hoc analyses in neuroimaging meta-analyses.

Coordinate-based meta-analysis (CBMA) is a powerful technique in the field of human brain imaging research. Due to its intense usage, several procedures for data preparation and post hoc analyses have been proposed so far. However, these steps are often performed manually by the researcher, and are therefore potentially prone to error and time-consuming. We hence developed the Coordinate-Based Meta-Analyses Toolbox (CBMAT) to provide a suite of user-friendly and automated MATLAB® functions allowing one to perform all these procedures in a fast, reproducible and reliable way. Besides the description of the code, in the present paper we also provide an annotated example of using CBMAT on a dataset including 34 experiments. CBMAT can therefore substantially improve the way data are handled when performing CBMAs. The code can be downloaded from https://github.com/Jordi-Manuello/CBMAT.git .

Passive Alzheimer's immunotherapy: A promising or uncertain option?

The US Food and Drug Administration (FDA)'s recent accelerated approval of two anti-amyloid antibodies for treatment of Alzheimer's disease (AD), aducanumab and lecanemab, has caused substantial debate. To inform this debate, we reviewed the literature on randomized clinical trials conducted with eight such antibodies focusing on clinical efficacy, cerebral amyloid removal, amyloid-related imaging abnormalities (ARIAs) and cerebral volumes to the extent such measurements have been reported. Two antibodies, donanemab and lecanemab, have demonstrated clinical efficacy, but these results remain uncertain. We further argue that the decreased amyloid PET signal in these trials is unlikely to be a one-to-one reflection of amyloid removal, but rather a reflection of increased therapy-related brain damage, as supported by the increased incidence of ARIAs and reported loss of brain volume. Due to these uncertainties of benefit and risk, we recommend that the FDA pauses existing approvals and approval of new antibodies until results of phase 4 studies with these drugs are available to inform on these risk-benefit uncertainties. We recommend that the FDA prioritize FDG PET and detection of ARIAs and accelerated brain volume loss with MRI in all trial patients, and neuropathological examination of all patients who die in these phase 4 trials.

FDG-PET versus Amyloid-PET Imaging for Diagnosis and Response Evaluation in Alzheimer's Disease: Benefits and Pitfalls.

In June 2021, the US Federal Drug and Food Administration (FDA) granted accelerated approval for the antibody aducanumab and, in January 2023, also for the antibody lecanemab, based on a perceived drug-induced removal of cerebral amyloid-beta as assessed by amyloid-PET and, in the case of lecanemab, also a presumption of limited clinical efficacy. Approval of the antibody donanemab is awaiting further data. However, published trial data indicate few, small and uncertain clinical benefits, below what is considered "clinically meaningful" and similar to the effect of conventional medication. Furthermore, a therapy-related decrease in the amyloid-PET signal may also reflect increased cell damage rather than simply "amyloid removal". This interpretation is more consistent with increased rates of amyloid-related imaging abnormalities and brain volume loss in treated patients, relative to placebo. We also challenge the current diagnostic criteria for AD based on amyloid-PET imaging biomarkers and recommend that future anti-AD therapy trials apply: (1) diagnosis of AD based on the co-occurrence of cognitive decline and decreased cerebral metabolism assessed by FDA-approved FDG-PET, (2) therapy efficacy determined by favorable effect on cognitive ability, cerebral metabolism by FDG-PET, and brain volumes by MRI, and (3) neuropathologic examination of all deaths occurring in these trials.

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.

Revealing the Selectivity of Neuroanatomical Alteration in Autism Spectrum Disorder via Reverse Inference.

BACKGROUND: Although neuroimaging research has identified atypical neuroanatomical substrates in individuals with autism spectrum disorder (ASD), it is at present unclear whether and to what extent disorder-selective gray matter alterations occur in this spectrum of conditions. In fact, a growing body of evidence shows a substantial overlap between the pathomorphological changes across different brain diseases, which may complicate identification of reliable neural markers and differentiation of the anatomical substrates of distinct psychopathologies. METHODS: Using a novel data-driven and Bayesian methodology with published voxel-based morphometry data (849 peer-reviewed experiments and 22,304 clinical subjects), this study performs the first reverse inference investigation to explore the selective structural brain alteration profile of ASD. RESULTS: We found that specific brain areas exhibit a >90% probability of gray matter alteration selectivity for ASD: the bilateral precuneus (Brodmann area 7), right inferior occipital gyrus (Brodmann area 18), left cerebellar lobule IX and Crus II, right cerebellar lobule VIIIA, and right Crus I. Of note, many brain voxels that are selective for ASD include areas that are posterior components of the default mode network. CONCLUSIONS: The identification of these spatial gray matter alteration patterns offers new insights into understanding the complex neurobiological underpinnings of ASD and opens attractive prospects for future neuroimaging-based interventions.

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.

Retrospective Bayesian Evidence of Null Effect in Two Decades of Alzheimer's Disease Clinical Trials.

Despite intense research on Alzheimer's disease, no validated treatment able to reverse symptomatology or stop disease progression exists. A recent systematic review by Kim and colleagues evaluated possible reasons behind the failure of the majority of the clinical trials. As the focus was on methodological factors, no statistical trends were examined in detail. Here, we aim to complete this picture leveraging on Bayesian analysis. In particular, we tested whether the failure of those clinical trials was essentially due to insufficient statistical power or to lack of a true effect. The strong Bayes' Factor obtained supported the latter hypothesis.

Linking neuroanatomical abnormalities in autism spectrum disorder with gene expression of candidate ASD genes: A meta-analytic and network-oriented approach.

BACKGROUND: Autism spectrum disorder (ASD) is a set of developmental conditions with widespread neuroanatomical abnormalities and a strong genetic basis. Although neuroimaging studies have indicated anatomical changes in grey matter (GM) morphometry, their associations with gene expression remain elusive. METHODS: Here, we aim to understand how gene expression correlates with neuroanatomical atypicalities in ASD. To do so, we performed a coordinate-based meta-analysis to determine the common GM variation pattern in the autistic brain. From the Allen Human Brain Atlas, we selected eight genes from the SHANK, NRXN, NLGN family and MECP2, which have been implicated with ASD, particularly in regards to altered synaptic transmission and plasticity. The gene expression maps for each gene were built. We then assessed the correlation between the gene expression maps and the GM alteration maps. Lastly, we projected the obtained clusters of GM alteration-gene correlations on top of the canonical resting state networks, in order to provide a functional characterization of the structural evidence. RESULTS: We found that gene expression of most genes correlated with GM alteration (both increase and decrease) in regions located in the default mode network. Decreased GM was also correlated with gene expression of some ASD genes in areas associated with the dorsal attention and cerebellar network. Lastly, single genes were found to be significantly correlated with increased GM in areas located in the somatomotor, limbic and ganglia/thalamus networks. CONCLUSIONS: This approach allowed us to combine the well beaten path of genetic and brain imaging in a novel way, to specifically investigate the relation between gene expression and brain with structural damage, and individuate genes of potential interest for further investigation in the functional domain.

Sex differences in brain homotopic co-activations: a meta-analytic study.

An element of great interest in functional connectivity is 'homotopic connectivity' (HC), namely the connectivity between two mirrored areas of the two hemispheres, mainly mediated by the fibers of the corpus callosum. Despite a long tradition of studying sexual dimorphism in the human brain, to our knowledge only one study has addressed the influence of sex on HC.We investigated the issue of homotopic co-activations in women and men using a coordinate-based meta-analytic method and data from the BrainMap database. A first unexpected observation was that the database was affected by a sex bias: women-only groups are investigated less often than men-only ones, and they are more often studied in certain domains such as emotion compared to men, and less in cognition. Implementing a series of sampling procedures to equalize the size and proportion of the datasets, our results indicated that females exhibit stronger interhemispheric co-activation than males, suggesting that the female brain is less lateralized and more integrated than that of males. In addition, males appear to show less intense but more extensive co-activation than females. Some local differences also appeared. In particular, it appears that primary motor and perceptual areas are more co-activated in males, in contrast to the opposite trend in the rest of the brain. This argues for a multidimensional view of sex brain differences and suggests that the issue should be approached with more complex models than previously thought.

Seeking Overlapping Neuroanatomical Alterations between Dyslexia and Attention-Deficit/Hyperactivity Disorder: A Meta-Analytic Replication Study.

The present work is a replication article based on the paper "Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies" by McGrath and Stoodley (2019). In the original research, the authors used activation likelihood estimation (ALE), a technique to perform coordinate-based meta-analysis (CBMA), to investigate the existence of brain regions undergoing gray matter alteration in association with both attention-deficit/hyper-activity disorder (ADHD) and dyslexia. Here, the same voxel-based morphometry dataset was analyzed, while using the permutation-subject images version of signed differential mapping (PSI-SDM) in place of ALE. Overall, the replication converged with the original paper in showing a limited overlap between the two conditions. In particular, no significant effect was found for dyslexia, therefore precluding any form of comparison between the two disorders. The possible influences of biological sex, age, and medication status were also ruled out. Our findings are in line with literature about gray matter alteration associated with ADHD and dyslexia, often showing conflicting results. Therefore, although neuropsychological and clinical evidence suggest some convergence between ADHD and dyslexia, more future research is sorely needed to reach a consensus on the neuroimaging domain in terms of patterns of gray matter alteration.

A Bayesian Reanalysis of the Phase III Aducanumab (ADU) Trial.

BACKGROUND: In December 2019, in light of additional blinded data, Biogen claimed efficacy of the drug Aducanumab (ADU). OBJECTIVE: We conducted a reanalysis of the phase III ADU summary statistics, focusing in particular on the Clinical Dementia Rating-Sum of Boxes. METHODS: We used a Bayesian framework to mitigate the problems of the null-hypothesis significance testing framework. In particular, we used Bayes Factor (BF) to analyze the summary statistics. The BF is the comparison of how well two hypotheses predict the data. RESULTS: Our results showed that the evidence for ADU efficacy is very low. The results show that the only data with a BF value in favor of the alternative hypothesis (i.e., drug efficacy) is the high-dose condition in the EMERGE trial. However, the obtained BF falls within the range of values considered anecdotal, meaning a low level of evidence. CONCLUSION: We provide a clearer interpretation of the results of the clinical trials based on the Bayesian framework, as this may be useful for future development and research in the field.