There is no easy fix to peer review but paying referees and regulating the number of submissions might help.

The exponential increase in the number of submissions, further accelerated by generative AI, and the decline in the availability of experts are burdening the peer review process. This has led to high unethical desk rejection rates, a growing appeal for the publication of unreviewed preprints, and a worrying proliferation of predatory journals. The idea of monetarily compensating peer reviewers has been around for many years; maybe, it is time to take it seriously as one way to save the peer review process. Here, I argue that paying reviewers, when done in a fair and transparent way, is a viable solution. Like the case of professional language editors, part-time or full-time professional reviewers, managed by universities or for-profit companies, can be an integral part of modern peer review. Being a professional reviewer could be financially attractive to retired senior researchers and to researchers who enjoy evaluating papers but are not motivated to do so for free. Moreover, not all produced research needs to go through peer review, and thus persuading researchers to limit submissions to their most novel and useful research could also help bring submission volumes to manageable levels. Overall, this paper reckons that the problem is not the peer review process per se but rather its function within an academic ecosystem dominated by an unhealthy culture of 'publish or perish'. Instead of reforming the peer review process, academia has to look for better science dissemination schemes that promote collaboration over competition, engagement over judgement, and research quality and sustainability over quantity.

7 T and beyond: toward a synergy between fMRI-based presurgical mapping at ultrahigh magnetic fields, AI, and robotic neurosurgery.

Presurgical evaluation with functional magnetic resonance imaging (fMRI) can reduce postsurgical morbidity. Here, we discuss presurgical fMRI mapping at ultra-high magnetic fields (UHF), i.e., ≥ 7 T, in the light of the current growing interest in artificial intelligence (AI) and robot-assisted neurosurgery. The potential of submillimetre fMRI mapping can help better appreciate uncertainty on resection margins, though geometric distortions at UHF might lessen the accuracy of fMRI maps. A useful trade-off for UHF fMRI is to collect data with 1-mm isotropic resolution to ensure high sensitivity and subsequently a low risk of false negatives. Scanning at UHF might yield a revival interest in slow event-related fMRI, thereby offering a richer depiction of the dynamics of fMRI responses. The potential applications of AI concern denoising and artefact removal, generation of super-resolution fMRI maps, and accurate fusion or coregistration between anatomical and fMRI maps. The latter can benefit from the use of T1-weighted echo-planar imaging for better visualization of brain activations. Such AI-augmented fMRI maps would provide high-quality input data to robotic surgery systems, thereby improving the accuracy and reliability of robot-assisted neurosurgery. Ultimately, the advancement in fMRI at UHF would promote clinically useful synergies between fMRI, AI, and robotic neurosurgery.Relevance statement This review highlights the potential synergies between fMRI at UHF, AI, and robotic neurosurgery in improving the accuracy and reliability of fMRI-based presurgical mapping.Key points• Presurgical fMRI mapping at UHF improves spatial resolution and sensitivity.• Slow event-related designs offer a richer depiction of fMRI responses dynamics.• AI can support denoising, artefact removal, and generation of super-resolution fMRI maps.• AI-augmented fMRI maps can provide high-quality input data to robotic surgery systems.

Effect of Comorbidities Features in Machine Learning Models for Survival Analysis to Predict Prodromal Alzheimer's Disease.

Alzheimer's Disease (AD) is the most common form of dementia, specifically a progressive degenerative disorder affecting 47 million people worldwide and is only expected to grow in the elderly population. The detection of AD in its early stages is crucial to allow early intervention aiding in the prevention or slowing down of the disease. The effect of using comorbidity features in machine learning models to predict the time until a patient develops a prodrome was observed. In this study, we used Alzheimer's Disease Neuroimaging Initiative (ADNI) high-dimensional clinical data to compare the performance of six machine learning algorithms for survival analysis, combined with six feature selection methods trained on two settings: with and without comorbidities features. Our ridge model combined with permutation feature selection achieves maximum performance of 0.90 when using comorbidity features with the concordance index as a performance indicator. This demonstrated that incorporating comorbidities into the feature set enhances the performance of survival analysis for Alzheimer's disease. There is potential to identify risk factors (coronary artery disease) from comorbidities which could guide preventative care based on medical history.

Cognitive decline assessment in speakers of understudied languages.

Projected trends in population aging have forecasted a massive increase in the number of people with dementia, in particular in sub-Saharan Africa and the Middle East and North Africa (MENA) region. Cognitive decline is a significant marker for dementia, typically assessed with standardized neuropsychological tools that have been validated in some well-researched languages such as English. However, with the existing language diversity, current tools cannot cater to speakers of understudied languages, putting these populations at a disadvantage when it comes to access to early and accurate diagnosis of dementia. Here, we shed light on the detrimental impact of this language gap in the context of the MENA region, highlighting inadequate tools and an unacceptable lack of expertise for a MENA population of a half billion people. Our perspective calls for more research to unravel the exact impact of the language gap on the quality of cognitive decline assessment in speakers of understudied languages. Highlights: Cognitive decline is a marker for dementia, assessed with neuropsychological tests.There is a lack of culturally valid tests for speakers of understudied languages.For example, suboptimal cognitive tests are used in the Middle East and North Africa region.Linguistic diversity should be considered in the development of cognitive tests.

Transformers for autonomous recognition of psychiatric dysfunction via raw and imbalanced EEG signals.

Early identification of mental disorders, based on subjective interviews, is extremely challenging in the clinical setting. There is a growing interest in developing automated screening tools for potential mental health problems based on biological markers. Here, we demonstrate the feasibility of an AI-powered diagnosis of different mental disorders using EEG data. Specifically, this work aims to classify different mental disorders in the following ecological context accurately: (1) using raw EEG data, (2) collected during rest, (3) during both eye open, and eye closed conditions, (4) at short 2-min duration, (5) on participants with different psychiatric conditions, (6) with some overlapping symptoms, and (7) with strongly imbalanced classes. To tackle this challenge, we designed and optimized a transformer-based architecture, where class imbalance is addressed through focal loss and class weight balancing. Using the recently released TDBRAIN dataset (n= 1274 participants), our method classifies each participant as either a neurotypical or suffering from major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), subjective memory complaints (SMC), or obsessive-compulsive disorder (OCD). We evaluate the performance of the proposed architecture on both the window-level and the patient-level. The classification of the 2-min raw EEG data into five classes achieved a window-level accuracy of 63.2% and 65.8% for open and closed eye conditions, respectively. When the classification is limited to three main classes (MDD, ADHD, SMC), window level accuracy improved to 75.1% and 69.9% for eye open and eye closed conditions, respectively. Our work paves the way for developing novel AI-based methods for accurately diagnosing mental disorders using raw resting-state EEG data.

An appraisal of the performance of AI tools for chronic stroke lesion segmentation.

Automated demarcation of stoke lesions from monospectral magnetic resonance imaging scans is extremely useful for diverse research and clinical applications, including lesion-symptom mapping to explain deficits and predict recovery. There is a significant surge of interest in the development of supervised artificial intelligence (AI) methods for that purpose, including deep learning, with a performance comparable to trained experts. Such AI-based methods, however, require copious amounts of data. Thanks to the availability of large datasets, the development of AI-based methods for lesion segmentation has immensely accelerated in the last decade. One of these datasets is the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset which includes T1-weighted images from hundreds of chronic stroke survivors with their manually traced lesions. This systematic review offers an appraisal of the impact of the ATLAS dataset in promoting the development of AI-based segmentation of stroke lesions. An examination of all published studies, that used the ATLAS dataset to both train and test their methods, highlighted an overall moderate performance (median Dice index = 59.40%) and a huge variability across studies in terms of data preprocessing, data augmentation, AI architecture, and the mode of operation (two-dimensional versus three-dimensional methods). Perhaps most importantly, almost all AI tools were borrowed from existing AI architectures in computer vision, as 90% of all selected studies relied on conventional convolutional neural network-based architectures. Overall, current research has not led to the development of robust AI architectures than can handle spatially heterogenous lesion patterns. This review also highlights the difficulty of gauging the performance of AI tools in the presence of uncertainties in the definition of the ground truth.

Interpreting and validating complexity and causality in lesion-symptom prognoses.

This paper considers the steps needed to generate pragmatic and interpretable lesion-symptom mappings that can be used for clinically reliable prognoses. The novel contributions are 3-fold. We first define and inter-relate five neurobiological and five methodological constraints that need to be accounted for when interpreting lesion-symptom associations and generating synthetic lesion data. The first implication is that, because of these constraints, lesion-symptom mapping needs to focus on probabilistic relationships between Lesion and Symptom, with Lesion as a multivariate spatial pattern, Symptom as a time-dependent behavioural profile and evidence that Lesion raises the probability of Symptom. The second implication is that in order to assess the strength of probabilistic causality, we need to distinguish between causal lesion sites, incidental lesion sites, spared but dysfunctional sites and intact sites, all of which might affect the accuracy of the predictions and prognoses generated. We then formulate lesion-symptom mappings in logical notations, including combinatorial rules, that are then used to evaluate and better understand complex brain-behaviour relationships. The logical and theoretical framework presented applies to any type of neurological disorder but is primarily discussed in relationship to stroke damage. Accommodating the identified constraints, we discuss how the 1965 Bradford Hill criteria for inferring probabilistic causality, post hoc, from observed correlations in epidemiology-can be applied to lesion-symptom mapping in stroke survivors. Finally, we propose that rather than rely on post hoc evaluation of how well the causality criteria have been met, the neurobiological and methodological constraints should be addressed, a priori, by changing the experimental design of lesion-symptom mappings and setting up an open platform to share and validate the discovery of reliable and accurate lesion rules that are clinically useful.

The elusive metric of lesion load.

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Multiple functions of the angular gyrus at high temporal resolution.

Here, the functions of the angular gyrus (AG) are evaluated in the light of current evidence from transcranial magnetic/electric stimulation (TMS/TES) and EEG/MEG studies. 65 TMS/TES and 52 EEG/MEG studies were examined in this review. TMS/TES literature points to a causal role in semantic processing, word and number processing, attention and visual search, self-guided movement, memory, and self-processing. EEG/MEG studies reported AG effects at latencies varying between 32 and 800 ms in a wide range of domains, with a high probability to detect an effect at 300-350 ms post-stimulus onset. A three-phase unifying model revolving around the process of sensemaking is then suggested: (1) early AG involvement in defining the current context, within the first 200 ms, with a bias toward the right hemisphere; (2) attention re-orientation and retrieval of relevant information within 200-500 ms; and (3) cross-modal integration at late latencies with a bias toward the left hemisphere. This sensemaking process can favour accuracy (e.g. for word and number processing) or plausibility (e.g. for comprehension and social cognition). Such functions of the AG depend on the status of other connected regions. The much-debated semantic role is also discussed as follows: (1) there is a strong TMS/TES evidence for a causal semantic role, (2) current EEG/MEG evidence is however weak, but (3) the existing arguments against a semantic role for the AG are not strong. Some outstanding questions for future research are proposed. This review recognizes that cracking the role(s) of the AG in cognition is possible only when its exact contributions within the default mode network are teased apart.

A Data-Based Approach for Selecting Pre- and Intra-Operative Language Mapping Tasks.

Background: Pre- and intra-operative language mapping in neurosurgery patients frequently involves an object naming task. The choice of the optimal object naming paradigm remains challenging due to lack of normative data and standardization in mapping practices. The aim of this study was to identify object naming paradigms that robustly and consistently activate classical language regions and could therefore be used to improve the sensitivity of language mapping in brain tumor and epilepsy patients. Methods: Functional magnetic resonance imaging (fMRI) data from two independent groups of healthy controls (total = 79) were used to generate threshold-weighted voxel-based consistency maps. This novel approach allowed us to compare inter-subject consistency of activation for naming single objects in the visual and auditory modality and naming two objects in a phrase or a sentence. Results: We found that the consistency of activation in language regions was greater for naming two objects per picture than one object per picture, even when controlling for the number of names produced in 5 s. Conclusion: More consistent activation in language areas for naming two objects compared to one object suggests that two-object naming tasks may be more suitable for delimiting language eloquent regions with pre- and intra-operative language testing. More broadly, we propose that the functional specificity of brain mapping paradigms for a whole range of different linguistic and non-linguistic functions could be enhanced by referring to databased models of inter-subject consistency and variability in typical and atypical brain responses.

Lesions that do or do not impair digit span: a study of 816 stroke survivors.

Prior studies have reported inconsistency in the lesion sites associated with verbal short-term memory impairments. Here we asked: How many different lesion sites can account for selective impairments in verbal short-term memory that persist over time, and how consistently do these lesion sites impair verbal short-term memory? We assessed verbal short-term memory impairments using a forward digit span task from the Comprehensive Aphasia Test. First, we identified the incidence of digit span impairments in a sample of 816 stroke survivors (541 males/275 females; age at stroke onset 56 ± 13 years; time post-stroke 4.4 ± 5.2 years). Second, we studied the lesion sites in a subgroup of these patients (n = 39) with left hemisphere damage and selective digit span impairment-defined as impaired digit span with unimpaired spoken picture naming and spoken word comprehension (tests of speech production and speech perception, respectively). Third, we examined how often these lesion sites were observed in patients who either had no digit span impairments or digit span impairments that co-occurred with difficulties in speech perception and/or production tasks. Digit span impairments were observed in 222/816 patients. Almost all (199/222 = 90%) had left hemisphere damage to five small regions in basal ganglia and/or temporo-parietal areas. Even complete damage to one or more of these five regions was not consistently associated with persistent digit span impairment. However, when the same regions were spared, only 5% (23/455) presented with digit span impairments. These data suggest that verbal short-term memory impairments are most consistently associated with damage to left temporo-parietal and basal ganglia structures. Sparing of these regions very rarely results in persistently poor verbal short-term memory. These findings have clinical implications for predicting recovery of verbal short-term memory after stroke.

Time-Dependent Reliability Analysis of Reinforced Concrete Beams Subjected to Uniform and Pitting Corrosion and Brittle Fracture.

Reinforced concrete (RC) beams are basic elements used in the construction of various structures and infrastructural systems. When exposed to harsh environmental conditions, the integrity of RC beams could be compromised as a result of various deterioration mechanisms. One of the most common deterioration mechanisms is the formation of different types of corrosion in the steel reinforcements of the beams, which could impact the overall reliability of the beam. Existing classical reliability analysis methods have shown unstable results when used for the assessment of highly nonlinear problems, such as corroded RC beams. To that end, the main purpose of this paper is to explore the use of a structural reliability method for the multi-state assessment of corroded RC beams. To do so, an improved reliability method, namely the three-term conjugate map (TCM) based on the first order reliability method (FORM), is used. The application of the TCM method to identify the multi-state failure of RC beams is validated against various well-known structural reliability-based FORM formulations. The limit state function (LSF) for corroded RC beams is formulated in accordance with two corrosion types, namely uniform and pitting corrosion, and with consideration of brittle fracture due to the pit-to-crack transition probability. The time-dependent reliability analyses conducted in this study are also used to assess the influence of various parameters on the resulting failure probability of the corroded beams. The results show that the nominal bar diameter, corrosion initiation rate, and the external loads have an important influence on the safety of these structures. In addition, the proposed method is shown to outperform other reliability-based FORM formulations in predicting the level of reliability in RC beams.

Damage to Broca's area does not contribute to long-term speech production outcome after stroke.

Broca's area in the posterior half of the left inferior frontal gyrus has long been thought to be critical for speech production. The current view is that long-term speech production outcome in patients with Broca's area damage is best explained by the combination of damage to Broca's area and neighbouring regions including the underlying white matter, which was also damaged in Paul Broca's two historic cases. Here, we dissociate the effect of damage to Broca's area from the effect of damage to surrounding areas by studying long-term speech production outcome in 134 stroke survivors with relatively circumscribed left frontal lobe lesions that spared posterior speech production areas in lateral inferior parietal and superior temporal association cortices. Collectively, these patients had varying degrees of damage to one or more of nine atlas-based grey or white matter regions: Brodmann areas 44 and 45 (together known as Broca's area), ventral premotor cortex, primary motor cortex, insula, putamen, the anterior segment of the arcuate fasciculus, uncinate fasciculus and frontal aslant tract. Spoken picture description scores from the Comprehensive Aphasia Test were used as the outcome measure. Multiple regression analyses allowed us to tease apart the contribution of other variables influencing speech production abilities such as total lesion volume and time post-stroke. We found that, in our sample of patients with left frontal damage, long-term speech production impairments (lasting beyond 3 months post-stroke) were solely predicted by the degree of damage to white matter, directly above the insula, in the vicinity of the anterior part of the arcuate fasciculus, with no contribution from the degree of damage to Broca's area (as confirmed with Bayesian statistics). The effect of white matter damage cannot be explained by a disconnection of Broca's area, because speech production scores were worse after damage to the anterior arcuate fasciculus with relative sparing of Broca's area than after damage to Broca's area with relative sparing of the anterior arcuate fasciculus. Our findings provide evidence for three novel conclusions: (i) Broca's area damage does not contribute to long-term speech production outcome after left frontal lobe strokes; (ii) persistent speech production impairments after damage to the anterior arcuate fasciculus cannot be explained by a disconnection of Broca's area; and (iii) the prior association between persistent speech production impairments and Broca's area damage can be explained by co-occurring white matter damage, above the insula, in the vicinity of the anterior part of the arcuate fasciculus.

An active human role is essential in big data-led decisions and data-intensive science.

Big data is transforming many sectors, with far-reaching consequences to how decisions are made and how knowledge is produced and shared. In the current move toward more data-led decisions and data-intensive science, we aim here to examine three issues that are changing the way data are read and used. First, there is a shift toward paradigms that involve a large amount of data. In such paradigms, the creation of complex data-led models becomes tractable and appealing to generate predictions and explanations. This necessitates for instance a rethinking of Occam's razor principle in the context of knowledge discovery. Second, there is a growing erosion of the human role in decision making and knowledge discovery processes. Human users' involvement is decreasing at an alarming rate, with no say on how to read, process, and summarize data. This makes legal responsibility and accountability hard to define. Third, thanks to its increasing popularity, big data is gaining a seductive allure, where volume and complexity of big data can de facto confer more persuasion and significance to knowledge or decisions that result from big-data-based processes. These issues call for an active human role by creating opportunities to incorporate, in the most unbiased way, human expertise and prior knowledge in decision making and knowledge production. This also requires putting in place robust monitoring and appraisal mechanisms to ensure that relevant data is answering the right questions. As the proliferation of data continues to grow, we need to rethink the way we interact with data to serve human needs.

Development of a New Internally Controlled One-Step Real-Time RT-PCR for the Molecular Detection of Enterovirus A71 in Africa and Madagascar.

Enterovirus A71 (EV-A71) is a leading cause of hand-foot-and-mouth disease (HFMD) and can be associated with severe neurological complications. EV-A71 strains can be classified into seven genogroups, A-H, on the basis of the VP1 capsid protein gene sequence. Genogroup A includes the prototype strain; genogroups B and C are responsible of major outbreaks worldwide, but little is known about the others, particularly genogroups E and F, which have been recently identified in Africa and Madagascar, respectively. The circulation of EV-A71 in the African region is poorly known and probably underestimated. A rapid and specific assay for detecting all genogroups of EV-A71 is required. In this study, we developed a real-time RT-PCR assay with a competitive internal control (IC). The primers and TaqMan probe specifically target the genomic region encoding the VP1 capsid protein. Diverse EV-A71 RNAs were successfully amplified from the genogroups A, B, C, D, E, and F, with similar sensitivity and robust reproducibility. Neither cross reaction with other EVs nor major interference with the competitive IC was detected. Experimentally spiked stool and plasma specimens provided consistent and reproducible results, and validated the usefulness of the IC for demonstrating the presence of PCR inhibitors in samples. The analysis in an African laboratories network of 1889 untyped enterovirus isolates detected 15 EV-A71 of different genogroups. This specific real-time RT-PCR assay provides a robust and sensitive method for the detection of EV-A71 in biological specimens and for the epidemiological monitoring of EV-A71 including its recently discovered genogroups.

Prediction of Lattice Constant of A2XY6 Cubic Crystals Using Gene Expression Programming.

Lattice constant is one of the paramount parameters that mark the quality of thin film fabrication. Numerous research efforts have been made to calculate and measure lattice constant, including experimental and empirical approaches. Not withstanding these efforts, a reliable and simple-to-use model is still needed to predict accurately this vital parameter. In this study, gene expression programming (GEP) approach was implemented to establish trustworthy model for prediction of the lattice constant of A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I) cubic crystals based on a comprehensive experimental database. The obtained results showed that the proposed GEP correlation provides excellent prediction performance with an overall average absolute relative deviation (AARD%) of 0.3596% and a coefficient of determination (R2) of 0.9965. Moreover, the comparison of the performance between the newly proposed correlation and the best pre-existing paradigms demonstrated that the established GEP correlation is more robust, reliable, and efficient than the prior models for prediction of lattice constant of A2XY6 cubic crystals.