Abstract Resting-state electroencephalography (rsEEG) yields robust indices of ageing, notably individual alpha peak frequency (iAPF), alpha power, and the aperiodic exponent. Whether these markers reflect stable traits or shift with cognitive exertion remains unresolved, with direct consequences for lifespan and clinical research. We parameterised periodic and aperiodic rsEEG activity before and after cognitive tasks in a lifespan cohort ( N = 390, aged 20–70), a five-year longitudinal follow-up ( N = 100), and an independent older-adult dataset completing a different task ( N = 71). Across datasets, cognitive exertion produced lifespan-wide iAPF slowing and increase in associated power. Notably, aperiodic shifts were age-dependent, with post-task steepening of the exponent in younger adults that progressively flattened with advancing age, resulting in a stronger effect of age on post-task exponents. Our results demonstrate that widely used spectral metrics exhibit acute state-dependency and post-task recordings offer a promising translational framework for indexing individual differences in healthy ageing and pathology. Trial registration Clinicaltrials.gov: NCT05155397

Previous studies identified atrophy-based Alzheimer's disease(AD) subtypes linked to distinct clinical symptoms, but their consistency across subtyping approaches remains unclear. This large-scale study evaluates subtype concordance using two data-driven approaches. In this work, we analyzed data from n=10,011 patients across 10 AD cohorts spanning Europe, the US, and Australia, extracting regional volumes using Freesurfer. To characterize atrophy heterogeneity in the AD continuum, we developed a two-step approach, Snowphlake (Staging NeurOdegeneration With PHenotype informed progression timeLine of biomarKErs), to identify subtypes and atrophy-event sequences within each subtype. Results were compared with SuStaIn (Subtype and Stage Inference), which jointly estimates subtypes and staging, using similar training and validation. Training included Aβ+ participants (n=1,195) and Aβ- cognitively unimpaired controls (n=1,692). We validated model-staging in a held-out clinical dataset (n=6,362) and an independent dataset (n=762), and assessed clinical significance in Aβ+ subsets(n=1,796 held-out; n=159 external). Concordance analysis evaluated consistency between methods. In the AD dementia(AD-D) training data, both Snowphlake and SuStaIn identified four subtypes. In the validation datasets, staging with both methods correlated with Mini-Mental State Examination(MMSE) scores. The Snowphlake subtypes assigned in Aβ+ validation datasets were associated with alterations in specific cognitive domains(Cohen's f:[0.15-0.33]). Similarly, the SuStaIn subtypes were also associated specific cognitive domains(Cohen's f:[0.17-0.34]). However, we observed low concordance between Snowphlake and SuStaIn, with 39.7% of AD-D patients grouped in concordant subtypes by both methods. In conclusion, Snowphlake and SuStaIn identified four atrophy-based subtypes that linked to distinct symptom profiles. While this highlights that the neuro-anatomically defined subtypes also meaningfully associate with different cognitive impairments at a group level, the low concordance between methods suggests that future research is needed to better understand the biological and methodological factors contributing to the observed variability.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment. Cross-validation experiments were performed to benchmark performance against other available strategies using data from 20 864 participants with and without cognitive impairment, spanning 11 prospective and retrospective cohorts and 43 scanners. Evaluation metrics assessed the ability to remove scanner-related variations in brain volumes (marker concordance between scanner pairs) while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results For each strategy, marker concordances between scanners were significantly better (P < .001) compared with preharmonized data. The proposed multiclass model achieved significantly higher concordance (mean, 0.75 ± 0.09 [SD]) than the Neuroharmony model trained on individuals without cognitive impairment (mean, 0.70 ± 0.11) and preserved disease-related signal (∆AUC [area under the receiver operating characteristic curve] = -0.006 ± 0.027) better than the Neuroharmony model trained on individuals with and without cognitive impairment that did not use the proposed extension (∆AUC = -0.091 ± 0.036). The marker concordance was better in scanners seen during training (concordance > 0.97) than unseen (concordance < 0.79), independent of cognitive status. Conclusion In a large-scale multicenter dataset, the proposed multiclass Neuroharmony model outperformed other available strategies for harmonizing brain volumetric data from unseen scanners in a clinical setting. Keywords: Image Postprocessing, MR Imaging, Dementia, Random Forest Supplemental material is available for this article. Published under a CC BY 4.0 license See also commentary by Haller in this issue.

The unprecedented increase in life expectancy presents a unique opportunity and the necessity to explore both healthy and pathological aspects of ageing. Electroencephalography (EEG) has been widely used to identify neuromarkers of cognitive ageing due to its affordability and richness in information. However, despite the growing volume of data and methodological advancements, the abundance of contradictory and non-reproducible findings has hindered clinical translation. To address these challenges, our study introduces a comprehensive workflow expanding on previous EEG studies and investigates various static and dynamic power and connectivity estimates as potential neuromarkers of cognitive ageing in a large dataset. We also assess the robustness of our findings by testing their susceptibility to band specification. Finally, we characterise our findings using functionally annotated brain networks to improve their interpretability and multi-modal integration. Our analysis demonstrates the effect of methodological choices on findings and that dynamic rather than static neuromarkers are not only more sensitive but also more robust. Consequently, they emerge as strong candidates for cognitive ageing neuromarkers. Moreover, we were able to replicate the most established EEG findings in cognitive ageing, such as alpha oscillation slowing, increased beta power, reduced reactivity across multiple bands, and decreased delta connectivity. Additionally, when considering individual variations in alpha band, we clarified that alpha power is characteristic of memory performance rather than ageing, highlighting its potential as a neuromarker for cognitive ageing. Finally, our approach using functionally annotated source reconstruction allowed us to provide insights into domain-specific electrophysiological mechanisms underlying memory performance and ageing.

Background: Structural MRI plays a pivotal role in the radiological workup for assessing neurodegeneration. Scanner-related differences hinder quantitative neuroradiological assessment of Alzheimer′s disease (QNAD). This study aims to train a machine-learning model to harmonize brain volumetric data of patients not encountered during model training. Method: Neuroharmony is a recently developed method that uses image quality metrics (IQM) as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between AD-pathology and IQM during harmonization, we developed a multi-class extension of Neuroharmony. We performed cross-validation experiments to benchmark performance against existing approaches using data from 20,864 participants comprising cognitively unimpaired (CU) and impaired (CI) individuals, spanning 11 cohorts and 43 scanners. Evaluation metrics assessed ability to remove scanner-related variations in brain volumes (biomarker concordance), while retaining the ability to delineate different diagnostic groups (preserving disease-related signal). Results: For each strategy, biomarker concordances between scanners were significantly better (p<10^(-6)) compared to pre-harmonized data. The proposed multi-class model achieved significantly higher concordance than the Neuroharmony model trained on CU individuals (CI: p<10^(-6), CU: p=0.02) and preserved disease-related signal better than the Neuroharmony model trained on all individuals without our proposed extension (ΔAUC=-0.09). The biomarker concordance was better in scanners seen during training (concordance >97%) than unseen (concordance <79%), independent of cognitive status. Conclusion: In a large-scale multi-center dataset, our proposed multi-class Neuroharmony model outperformed other strategies available for harmonizing brain-volumetric data in a clinical setting. This paves the way for enabling QNAD in the future. ### Competing Interest Statement F.B. is on the steering committee or Data Safety Monitoring Board member for Biogen, Merck, ATRI/ACTC and Prothena. F.B. has been a consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen, Combinostics and has research agreements with Merck, Biogen, GE Healthcare, Roche. F.B. and D.C.A. are co-founders and shareholders of Queen Square Analytics Ltd. N.P.O. is a consultant for Queen Square Analytics Ltd. ### Funding Statement This study was supported by the Early Detection of Alzheimer's Disease Subtypes (E-DADS) project, an EU Joint Programme - Neurodegenerative Disease Research (JPND) project (see www.jpnd.eu). The project is supported under the aegis of JPND through the following funding organizations: United Kingdom, Medical Research Council (MR/T046422/1); Netherlands, ZonMW (733051106); France, Agence Nationale de la Recherche (ANR-19-JPW2- 000); Italy, Italian Ministry of Health (MoH); Australia, National Health & Medical Research Council (1191535); Hungary, National Research, Development and Innovation Office (2019-2.1.7-ERA-NET-2020-00008). This work used the Dutch national e-infrastructure with the support of the SURF Cooperative using grant no. EINF-5353. F.B. is supported by the NIHR Biomedical Research Centre at UCLH. B.W. and Z.V. were supported by Project no. RRF-2.3.1-21-2022-00015, which has been implemented with the support provided by the European Union. B.W. was supported by the Consolidator Researcher program of the Óbuda University. N.P.O. is supported by a UKRI Future Leaders Fellowship (UK Medical Research Council MR/S03546X/1). Acknowledgments The authors would like to thank the developers of the original Neuroharmony algorithm for making the code available, and all the research participants and their families for donating their data for scientific research. Data collection and sharing for ADNI was funded by the Alzheimer′s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer′s Association; Alzheimer′s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education , and the study is coordinated by the Alzheimer′s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The AIBL study (https://aibl.org.au) is a consortium between Austin Health, CSIRO, Edith Cowan University, the Florey Institute (The University of Melbourne), and the National Ageing Research Institute. The study has received partial financial support from the Alzheimer′s Association (US), the Alzheimer′s Drug Discovery Foundation, an Anonymous foundation, the Science and Industry Endowment Fund, the Dementia Collaborative Research Centres, the Victorian Government′s Operational Infrastructure Support program, the Australian Alzheimer′s Research Foundation, the National Health and Medical Research Council (NHMRC), and The Yulgilbar Foundation. Numerous commercial interactions have supported data collection and analyses. In-kind support has also been provided by Sir Charles Gairdner Hospital, Cogstate Ltd, Hollywood Private Hospital, The University of Melbourne, and St Vincent′s Hospital. The AIBL team wishes to thank all clinicians who referred patients with AD and/or MCI to the study. We also thank all those who took part as subjects in the study for their commitment and dedication to helping advance research into the early detection and causation of AD. We thank all the investigators within the AIBL who contributed to the design and implementation of the resource and/or provided data but did not actively participate in the development, analysis, interpretation or writing of this current study. The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, PhD), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, PhD), P20 AG068053 (PI Justin Miller, PhD), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli gave ethical approval for this work; Ethics committee/IRB of Amsterdam UMC gave ethical approval for this work; Ethics committee/IRB of Research Centre for Natural Sciences, Budapest gave ethical approval for this work; I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes ADC data can be made available to academic researchers upon reasonable request; ADNI and AIBL data are stored at the Laboratory of Neuroimaging at the University of Southern California and are available to the general scientific community for download: http://adni.loni.usc.edu; ArWiBO, EDSD, I-ADNI, OASIS and PharmaCog data are available for all researchers on the NeuGRID2 platform: https://www.neugrid2.eu/ (https://doi.org/10.17616/R31NJN1E); HuBA data can be made available upon reasonable request; NACC data is available through the National Alzheimer′s Coordinating Center platform: https://naccdata.org/; UKBB data is available at the UK Biobank platform: https://www.ukbiobank.ac.uk/;

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

One of the significant challenges in real-time fMRI environments is to ensure that the functional images are exported in real-time. The prerequired ability to reconstruct these images immediately after the acquisition has already been resolved in 2004. Nowadays, more sophisticated sequences allow for higher resolution and faster repetition times and thereby challenging the ability to export this data in real-time. In this article, we tackle the potentially arising problem of sending the reconstructed data from the MRI to an external PC to perform the real-time fMRI analysis. We show that depending on the implementation of the data transfer, long delays can occur that can differ drastically in time and how often they occur. In addition, we propose a solution for SIEMENS MRI devices which was tested and applied already on multiple MRI devices including 3T and 7T machines on different vendor software versions. This new technique can be used as a blueprint that can be directly applied to other manufacturers. We also provide the source code of the described solution and show that the delay in the data transfer can be significantly reduced to a tolerable level using our proposed procedure. Finally, we integrate measurement options for the data transfer times to improve quality measures in (clinical) real-time fMRI environments that can implement the proposed solution. Efforts should be taken by the real-time fMRI community and MRI manufacturers to employ a standardized real-time export e.g., similar to the lab streaming layer which is used as a standard export method in EEG environments.

Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.

The Brain Imaging Data Structure (BIDS) established community consensus on the organization of data and metadata for several neuroimaging modalities. Traditionally, BIDS had a strong focus on functional magnetic resonance imaging (MRI) datasets and lacked guidance on how to store multimodal structural MRI datasets. Here, we present and describe the BIDS Extension Proposal 001 (BEP001), which adds a range of quantitative MRI (qMRI) applications to the BIDS. In general, the aim of qMRI is to characterize brain microstructure by quantifying the physical MR parameters of the tissue via computational, biophysical models. By proposing this new standard, we envision standardization of qMRI through multicenter dissemination of interoperable datasets. This way, BIDS can act as a catalyst of convergence between qMRI methods development and application-driven neuroimaging studies that can help develop quantitative biomarkers for neural tissue characterization. In conclusion, this BIDS extension offers a common ground for developers to exchange novel imaging data and tools, reducing the entrance barrier for qMRI in the field of neuroimaging.

While writing this abstract I received an email, which I promptly answered. When I returned my attention to the abstract, I struggled to regain my flow of writing. In order to understand this deficit in performance associated with switching from one task to another, or "switch cost", cognitive neuroscientists use task switching paradigms to recreate similar experiences. However, many researchers may be familiar with the difficulties that accompany modifying an established paradigm to suit their experimental design, or even the challenge of creating a new, unvalidated paradigm to perturb a particular aspect of cognitive function. This software tool article introduces a novel task switching paradigm for use and adaptation in online and neuroimaging task switching studies. The paradigm was constructed with a flexible, easily-adapted framework that can accommodate a variety of designs. This paradigm utilizes three psychometrically opposed but visually similar tasks- the Digit Span, the Spatial Span, and the Spatial Rotation. In two Use Cases we demonstrate the reliable nature of overall task performance and the dependence of switch costs on certain task parameters. This task framework can be adapted for use across different experimental designs and environment, and we encourage researchers to modify the task switching game for their experiments.

Low-intensity transcranial electrical stimulation (tES), including alternating or direct current stimulation, applies weak electrical stimulation to modulate the activity of brain circuits. Integration of tES with concurrent functional MRI (fMRI) allows for the mapping of neural activity during neuromodulation, supporting causal studies of both brain function and tES effects. Methodological aspects of tES-fMRI studies underpin the results, and reporting them in appropriate detail is required for reproducibility and interpretability. Despite the growing number of published reports, there are no consensus-based checklists for disclosing methodological details of concurrent tES-fMRI studies. The objective of this work was to develop a consensus-based checklist of reporting standards for concurrent tES-fMRI studies to support methodological rigor, transparency and reproducibility (ContES checklist). A two-phase Delphi consensus process was conducted by a steering committee (SC) of 13 members and 49 expert panelists through the International Network of the tES-fMRI Consortium. The process began with a circulation of a preliminary checklist of essential items and additional recommendations, developed by the SC on the basis of a systematic review of 57 concurrent tES-fMRI studies. Contributors were then invited to suggest revisions or additions to the initial checklist. After the revision phase, contributors rated the importance of the 17 essential items and 42 additional recommendations in the final checklist. The state of methodological transparency within the 57 reviewed concurrent tES-fMRI studies was then assessed by using the checklist. Experts refined the checklist through the revision and rating phases, leading to a checklist with three categories of essential items and additional recommendations: (i) technological factors, (ii) safety and noise tests and (iii) methodological factors. The level of reporting of checklist items varied among the 57 concurrent tES-fMRI papers, ranging from 24% to 76%. On average, 53% of checklist items were reported in a given article. In conclusion, use of the ContES checklist is expected to enhance the methodological reporting quality of future concurrent tES-fMRI studies and increase methodological transparency and reproducibility.

Real-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well as time-series of target brain areas and resting-state networks. We estimated recursive temporal signal-to-noise ratio (rtSNR) and contrast-to-noise ratio (rtCNR), and real-time head motion parameters by a framewise rigid-body transformation (translations and rotations) using the conventional current to template volume registration. In addition, we derived real-time framewise (FD) and micro (MD) displacements based on head motion parameters and evaluated the temporal derivative of root mean squared variance over voxels (DVARS). For monitoring time-series of target regions and networks, we estimated the number of spikes and amount of filtered noise by means of a modified Kalman filter. Finally, we applied the incremental general linear modeling (GLM) to evaluate real-time contributions of nuisance regressors (linear trend and head motion). Proposed rtQA was demonstrated in real-time fMRI neurofeedback runs without and with excessive head motion and real-time simulations of neurofeedback and resting-state fMRI data. The rtQA was implemented as an extension of the open-source OpenNFT software written in Python, MATLAB and C++ for neurofeedback, task-based, and resting-state paradigms. We also developed a general Python library to unify real-time fMRI data processing and neurofeedback applications. Flexible estimation and visualization of rtQA facilitates efficient rtQA of fMRI data and helps the robustness of fMRI acquisitions by means of substantiating decisions about the necessity of the interruption and re-start of the experiment and increasing the confidence in neural estimates.

Depression is the leading cause of disability worldwide, with prevalence rates rising. Despite the scale of the problem, available pharmacological and psychological interventions only have limited efficacy. The National Institute of Health's Science of Behaviour Change framework proposes to address this issue by capitalising on insights from basic science to identify mechanisms that can be targeted by novel interventions. The current study evaluated the potential of a computerized programme aimed at improving affective control, a mechanistic target involved in both risk and maintenance of depression. In a first phase the cognitive profiles of 48 depressed in-dividuals (mean age: 39 years, 75 % female) were compared to cognitive functioning in 16 never-depressed individuals (mean age: 31 years, 56 % female). The sole index of functioning that differed between diagnostic groups was reaction time across negative and positively valanced trials on an affective Stroop task (d = 0.58). This index was then used to evaluate an affective control training (AffeCT) against a placebo training. Results showed no significant changes on tasks that showed no differences with never-depressed individuals in Phase I. However, compared to placebo training, AffeCT led to significantly greater improvement in the target index, affective Stroop performance (d = 1.17). Importantly, AffeCT led to greater reductions in negative affect as measured by the Positive Affect and Negative Affect Schedule compared to the placebo training (d = 0.98). This proof-of-concept study shows promising benefits of AffeCT on depressed individuals' affect, but not depressive symptoms. It further supports the utility of the Science of Behaviour Change framework, highlighting the need for determining meaningful assays of target mechanisms when evaluating novel interventions.

ABSTRACTOne of the significant challenges in real-time fMRI environments is to ensure that the functional images are exported in real-time. The prerequired ability to reconstruct these images immediately after the acquisition has already been resolved in 2004. Nowadays, more sophisticated sequences allow for higher resolution and faster repetition times and thereby challenging the ability to export this data in real-time. In this article, we tackle the potentially arising problem of sending the reconstructed data from the MRI to an external PC to perform the real-time fMRI analysis. We show that depending on the implementation of the data transfer, long delays can occur that can differ drastically in time and how often they occur. In addition, we propose a solution for SIEMENS MRI devices which was tested and applied already on multiple MRI devices including 3T and 7T machines on different vendor software versions. This new technique can be used as a blueprint that can be directly applied to other manufacturers. We also provide the source code of the described solution and show that the delay in the data transfer can be significantly reduced to a tolerable level using our proposed procedure. Finally, we integrate measurement options for the data transfer times to improve quality measures in real-time fMRI environments (e.g., clinical) that can implement the proposed solution. Efforts should be taken by the real-time community and MRI manufacturers to employ a standardized real-time export e.g., similar to the lab streaming layer which is used as a standard export method in EEG environments.

As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of studies have outlined how moving online made it possible to gather a more global community and has increased opportunities for individuals with various constraints, e.g., caregiving responsibilities. Yet, the mere existence of online conferences is no guarantee that everyone can attend and participate meaningfully. In fact, many elements of an online conference are still significant barriers to truly diverse participation: the tools used can be inaccessible for some individuals; the scheduling choices can favour some geographical locations; the set-up of the conference can provide more visibility to well-established researchers and reduce opportunities for early-career researchers. While acknowledging the benefits of an online setting, especially for individuals who have traditionally been underrepresented or excluded, we recognize that fostering social justice requires inclusivity to actively be centered in every aspect of online conference design. Here, we draw from the literature and from our own experiences to identify practices that purposefully encourage a diverse community to attend, participate in, and lead online conferences. Reflecting on how to design more inclusive online events is especially important as multiple scientific organizations have announced that they will continue offering an online version of their event when in-person conferences can resume.

Neurofeedback (NF) is a complex learning scenario, as the task consists of trying out mental strategies while processing a feedback signal that signifies activation in the brain area to be self-regulated and acts as a potential reward signal. In an attempt to dissect these subcomponents, we obtained whole-brain networks associated with efficient self-regulation in two paradigms: parallel, where the task was performed concurrently, combining feedback with strategy execution; and serial, where the task was performed consecutively, separating feedback processing from strategy execution. Twenty participants attempted to control their anterior midcingulate cortex (aMCC) using functional magnetic resonance imaging (fMRI) NF in 18 sessions over 2 weeks, using cognitive and emotional mental strategies. We analyzed whole-brain fMRI activations in the NF training runs with the largest aMCC activation for the serial and parallel paradigms. The equal length of the strategy execution and the feedback processing periods in the serial paradigm allows a description of the two task subcomponents with equal power. The resulting activation maps were spatially correlated with functionally annotated intrinsic connectivity brain maps (BMs). Brain activation in the parallel condition correlates with the basal ganglia (BG) network, the cingulo-opercular network (CON), and the frontoparietal control network (FPCN); brain activation in the serial strategy execution condition with the default mode network (DMN), the FPCN, and the visual processing network; while brain activation in the serial feedback processing condition predominantly with the CON, the DMN, and the FPCN. Additional comparisons indicate that BG activation is characteristic to the parallel paradigm, while supramarginal gyrus (SMG) and superior temporal gyrus (STG) activations are characteristic to the serial paradigm. The multifaceted view of the subcomponents allows describing the cognitive processes associated with strategy execution and feedback processing independently in the serial feedback task and as combined processes in the multitasking scenario of the conventional parallel feedback task.

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.

Tomas Ros, Stefanie Enriquez-Geppert, Vadim Zotev, Kymberly D. Young, Guilherme Wood, Susan Whitfield-Gabrieli, Feng Wan, Patrik Vuilleumier, François Vialatte, Dimitri Van De Ville, Doron Todder, Tanju Surmeli, James S. Sulzer, Ute Strehl, Maurice Barry Sterman, Naomi J. Steiner, Bettina Sorger, Surjo R. Soekadar, Ranganatha Sitaram, Leslie H. Sherlin, Michael Schönenberg, Frank Scharnowski, Manuel Schabus, Katya Rubia, Agostinho Rosa, Miriam Reiner, Jaime A. Pineda, Christian Paret, Alexei Ossadtchi, Andrew A. Nicholson, Wenya Nan, Javier Minguez, Jean-Arthur Micoulaud-Franchi, David M.A. Mehler, Michael Lührs, Joel Lubar, Fabien Lotte, David E.J. Linden, Jarrod A. Lewis-Peacock, Mikhail A. Lebedev, Ruth A. Lanius, Andrea Kübler, Cornelia Kranczioch, Yury Koush, Lilian Konicar, Simon H. Kohl, Silivia E. Kober, Manousos A. Klados, Camille Jeunet, T.W.P. Janssen, Rene J. Huster, Kerstin Hoedlmoser, Laurence M. Hirshberg, Stephan Heunis, Talma Hendler, Michelle Hampson, Adrian G. Guggisberg, Robert Guggenberger, John H. Gruzelier, Rainer W. Göbel, Nicolas Gninenko, Alireza Gharabaghi, Paul Frewen, Thomas Fovet, Thalı́a Fernández, Carlos Escolano, Ann-Christine Ehlis, Renate Drechsler, R. Christopher deCharms, Stefan Debener, Dirk De Ridder, Eddy J. Davelaar, Marco Congedo, Marc Cavazza, Marinus H.M. Breteler, Daniel Brandeis, Jerzy Bodurka, Niels Birbaumer, Olga M. Bazanova, Beatrix Barth, Panagiotis D. Bamidis, Tibor Auer, Martijn Arns and Robert T. Thibault

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large inter-individual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pretraining functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pretraining activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

In recent years, deep learning (DL) has become more widespread in the fields of cognitive and clinical neuroimaging. Using deep neural network models to process neuroimaging data is an efficient method to classify brain disorders and identify individuals who are at increased risk of age-related cognitive decline and neurodegenerative disease. Here we investigated, for the first time, whether structural brain imaging and DL can be used for predicting a physical trait that is of significant clinical relevance—the body mass index (BMI) of the individual. We show that individual BMI can be accurately predicted using a deep convolutional neural network (CNN) and a single structural magnetic resonance imaging (MRI) brain scan along with information about age and sex. Localization maps computed for the CNN highlighted several brain structures that strongly contributed to BMI prediction, including the caudate nucleus and the amygdala. Comparison to the results obtained via a standard automatic brain segmentation method revealed that the CNN-based visualization approach yielded complementary evidence regarding the relationship between brain structure and BMI. Taken together, our results imply that predicting BMI from structural brain scans using DL represents a promising approach to investigate the relationship between brain morphological variability and individual differences in body weight and provide a new scope for future investigations regarding the potential clinical utility of brain-predicted BMI.