Conversation is a primary means of social influence, but its effects on brain activity remain unknown. Previous work on conversation and social influence has emphasized public compliance, largely setting private beliefs aside. Here, we show that consensus-building conversation aligns future brain activity within groups, with alignment persisting through novel experiences participants did not discuss. Participants watched ambiguous movie clips during fMRI scanning, then conversed in groups with the goal of coming to a consensus about each clip\'s narrative. After conversation, participants\' brains were scanned while viewing the clips again, along with novel clips from the same movies. Groups that reached consensus showed greater similarity of brain activity after conversation. Participants perceived as having high social status spoke more and signaled disbelief in others, and their groups had unequal turn-taking and lower neural alignment. By contrast, participants with central positions in their real-world social networks encouraged others to speak, facilitating greater group neural alignment. Socially central participants were also more likely to become neurally aligned to others in their groups.

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain. However, the many QSM approaches available have generated a need in the neuroimaging community for guidelines on implementation. This article outlines considerations and implementation recommendations for QSM data acquisition, processing, analysis, and publication. We recommend that data be acquired using a monopolar 3D multi-echo gradient echo (GRE) sequence and that phase images be saved and exported in Digital Imaging and Communications in Medicine (DICOM) format and unwrapped using an exact unwrapping approach. Multi-echo images should be combined before background field removal, and a brain mask created using a brain extraction tool with the incorporation of phase-quality-based masking. Background fields within the brain mask should be removed using a technique based on SHARP or PDF, and the optimization approach to dipole inversion should be employed with a sparsity-based regularization. Susceptibility values should be measured relative to a specified reference, including the common reference region of the whole brain as a region of interest in the analysis. The minimum acquisition and processing details required when reporting QSM results are also provided. These recommendations should facilitate clinical QSM research and promote harmonized data acquisition, analysis, and reporting.

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual\'s tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

Theories of human learning converge on the view that individuals working together learn better than do those working independently. Little is known, however, about the neural mechanisms of learning through cooperation. We addressed this research gap by leveraging functional near-infrared spectroscopy to record the brain activity of triad members in a group simultaneously. Triads were instructed to analyze an ancient Chinese poem either cooperatively or independently. Four main findings emerged. First, we observed significant within-group neural synchronization (GNS) in the left superior temporal cortex, supramarginal gyrus, and postcentral gyrus during cooperative learning compared with independent learning. Second, the enhancement of GNS in triads was amplified when a consensus was reached (vs. elaboration or argument) during cooperative learning. Third, GNS was predictive of learning outcome at an early stage (156-170 s after learning was initiated). Fourth, social factors such as social closeness (e.g. how much learners liked one other) were reflected in GNS and co-varied with learning engagement. These results provide neuroscientific support for Piaget\'s theory of cognitive development and favor the notion that successful learning through cooperation involves dynamic consensus-building, which is captured in neural patterns shared across learners in a group.

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain\'s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show \"core\" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

Currently, the neuropathological diagnosis of Lewy body disease (LBD) may be stated according to several staging systems, which include the Braak Lewy body stages (Braak), the consensus criteria by McKeith and colleagues (McKeith), the modified McKeith system by Leverenz and colleagues (Leverenz), and the Unified Staging System by Beach and colleagues (Beach). All of these systems use semi-quantitative scoring (4- or 5-tier scales) of Lewy pathology (LP; i.e., Lewy bodies and Lewy neurites) in defined cortical and subcortical areas. While these systems are widely used, some suffer from low inter-rater reliability and/or an inability to unequivocally classify all cases with LP. To address these limitations, we devised a new system, the LP consensus criteria (LPC), which is based on the McKeith system, but applies a dichotomous approach for the scoring of LP (i.e., \"absent\" vs. \"present\") and includes amygdala-predominant and olfactory-only stages. α-Synuclein-stained slides from brainstem, limbic system, neocortex, and olfactory bulb from a total of 34 cases with LP provided by the Newcastle Brain Tissue Resource (NBTR) and the University of Pennsylvania brain bank (UPBB) were scanned and assessed by 16 raters, who provided diagnostic categories for each case according to Braak, McKeith, Leverenz, Beach, and LPC systems. In addition, using LP scores available from neuropathological reports of LP cases from UPBB (n = 202) and NBTR (n = 134), JT (UPBB) and JA (NBTR) assigned categories according to all staging systems to these cases. McKeith, Leverenz, and LPC systems reached good (Krippendorff\'s α ≈ 0.6), while both Braak and Beach systems had lower (Krippendorff\'s α ≈ 0.4) inter-rater reliability, respectively. Using the LPC system, all cases could be unequivocally classified by the majority of raters, which was also seen for 97.1% when the Beach system was used. However, a considerable proportion of cases could not be classified when using Leverenz (11.8%), McKeith (26.5%), or Braak (29.4%) systems. The category of neocortical LP according to the LPC system was associated with a 5.9 OR (p < 0.0001) of dementia in the 134 NBTR cases and a 3.14 OR (p = 0.0001) in the 202 UPBB cases. We established that the LPC system has good reproducibility and allows classification of all cases into distinct categories. We expect that it will be reliable and useful in routine diagnostic practice and, therefore, suggest that it should be the standard future approach for the basic post-mortem evaluation of LP.

Using visual evoked fields (VEFs) to differentiate healthy, normal brain function from dysfunctional cortex has been demonstrated to be both valid and reliable. Currently, VEFs are widely implemented to guide intracranial surgeries for epilepsy and brain tumors. There are several areas of possible future clinical use of VEFs, including early identification of disorders, such as multiple sclerosis, Parkinson\'s disease, stroke, and human immunodeficiency virus-associated neurocognitive disorders. These studies have suggested that VEFs could be used to study disease pathophysiology or as a biomarker for early identification of a disorder. The current clinical practice guidelines of the American Clinical Magnetoencephalography Society for VEFs are sufficient. At this time, VEFs should be used clinically to identify visual cortex and potentially tailor surgical resections.

Auditory evoked fields (AEFs) are well suited for studies of auditory processing in patients. Their sources have been localized to Heschl\'s gyri and to the supratemporal auditory cortices. Auditory evoked fields are known to be modulated by peripheral and central lesions of auditory pathways and to reflect group-level pathophysiology of neurodevelopmental and psychiatric disorders. They are useful in lateralization of language processes for planning neurosurgery and for localization of language-related cortex. The recently developed artifact rejection and movement compensation methods will enhance and extend the use of AEFs in studies of clinical patients and pediatric groups. New pediatric magnetoencephalography systems will facilitate clinical AEF studies of developmental disorders. In addition to their established use in planning neurosurgery, AEF findings in several new clinical patient groups suffering, e.g., from developmental, neurodegenerative, or psychiatric disorders have been reported. Several recent investigations report the correlations with clinical symptoms and sensitivity and specificity profiles of AEFs in studies of these disorders; this development is mandatory in gaining wider clinical approval for the use of AEFs in clinical practice dealing with individual patients. Most promising future research lines of clinical applicability of AEFs focus on developmental and psychiatric disorders.

In this article, we present the clinical indications and advances in the use of magnetoencephalography to map the primary sensorimotor (SM1) cortex in neurosurgical patients noninvasively. We emphasize the advantages of magnetoencephalography over sensorimotor mapping using functional magnetic resonance imaging. Recommendations to the referring physicians and the clinical magnetoencephalographers to achieve appropriate sensorimotor cortex mapping using magnetoencephalography are proposed. We finally provide some practical advice for the use of corticomuscular coherence, cortico-kinematic coherence, and mu rhythm suppression in this indication. Magnetoencephalography should now be considered as a method of reference for presurgical functional mapping of the sensorimotor cortex.