Consistent resting-state networks across healthy subjects

The phobic brain: Morphometric features correctly classify individuals with small animal phobia

Specific phobia represents an anxiety disorder category characterized by intense fear generated by specific stimuli. Among specific phobias, small animal phobia (SAP) denotes a particular condition that has been poorly investigated in the neuroscientific literature. Moreover, the few previous studies on this topic have mostly employed univariate analyses, with limited and unbalanced samples, leading to inconsistent results. To overcome these limitations, and to characterize the neural underpinnings of SAP, this study aims to develop a classification model of individuals with SAP based on gray matter features, by using a machine learning method known as the binary support vector machine. Moreover, the contribution of specific structural macro-networks, such as the default mode, the salience, the executive, and the affective networks, in separating phobic subjects from controls was assessed. Thirty-two subjects with SAP and 90 matched healthy controls were tested to this aim. At a whole-brain level, we found a significant predictive model including brain structures related to emotional regulation, cognitive control, and sensory integration, such as the cerebellum, the temporal pole, the frontal cortex, temporal lobes, the amygdala and the thalamus. Instead, when considering macro-networks analysis, we found the Default, the Affective, and partially the Central Executive and the Sensorimotor networks, to significantly outperform the other networks in classifying SAP individuals. In conclusion, this study expands knowledge about the neural basis of SAP, proposing new research directions and potential diagnostic strategies.

Near-critical corticothalamic eigenmodes: Effects of nonuniform connectivity on modes, activity, and communication channels

The effects of nonuniformities in axonal connectivity on natural modes of brain activity are explored to determine their contributions to modal eigenvalues, structure, and communication and to clarify the limits of validity of widely used uniform-connectivity approximations. Preferred channels of communication are demonstrated that are supported by natural modes of mean connectivity and resulting activity. The effects of axonal tracts on these modes are calculated using perturbation methods, and it is found that modes and their spectra are only moderately perturbed by even the largest white matter tracts. However, perturbations of activity are greatly magnified when modes are near-critical and realistic connectivity and gain perturbations can then enable rapid responses to stimuli on the observed timescales of evoked responses. It is thus argued that dynamic mode-mode communication channels complement ones based on white matter tracts and that both rely on near-criticality to have their observed effects.

Synchronization in spiking neural networks with short and long connections and time delays

Synchronization is fundamental for information processing in oscillatory brain networks and is strongly affected by time delays via signal propagation along long fibers. Their effect, however, is less evident in spiking neural networks given the discrete nature of spikes. To bridge the gap between these different modeling approaches, we study the synchronization conditions, dynamics underlying synchronization, and the role of the delay of a two-dimensional network model composed of adaptive exponential integrate-and-fire neurons. Through parameter exploration of neuronal and network properties, we map the synchronization behavior as a function of unidirectional long-range connection and the microscopic network properties and demonstrate that the principal network behaviors comprise standing or traveling waves of activity and depend on noise strength, E/I balance, and voltage adaptation, which are modulated by the delay of the long-range connection. Our results show the interplay of micro- (single neuron properties), meso- (connectivity and composition of the neuronal network), and macroscopic (long-range connectivity) parameters for the emergent spatiotemporal activity of the brain.

Symmetry breaking organizes the brain's resting state manifold

Spontaneously fluctuating brain activity patterns that emerge at rest have been linked to the brain's health and cognition. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a mechanistic description of the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major data features across scales and imaging modalities. These include spontaneous high-amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability, and characteristic functional connectivity dynamics. When aggregated across cortical hierarchies, these match the profiles from empirical data. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function. In addition, it shifts the focus from the single recordings towards the brain's capacity to generate certain dynamics characteristic of health and pathology.

Stepwise pathways from the olfactory cortex to central hub regions in the human brain

The human brain is organized as a hierarchical global network. Functional connectivity research reveals that sensory cortices are connected to corresponding association cortices via a series of intermediate nodes linked by synchronous neural activity. These sensory pathways and relay stations converge onto central cortical hubs such as the default-mode network (DMN). The DMN regions are believed to be critical for representing concepts and, hence, language acquisition and use. Although prior research has established that major senses are placed at a similar distance from the DMN-five to six connective steps-it is still unknown how the olfactory system functionally connects to the large-scale cortical hubs of the human brain. In this study, we investigated the connective distance from olfactory seed areas to the DMN. The connective distance involves a series of three to four intermediate steps. Furthermore, we parcellated the olfactory cortical subregions and found evidence of two distinct olfactory pathways. One emerges from the anterior olfactory nucleus and olfactory tubercle; it involves early access to the orbitofrontal cortex, known for processing reward and multisensory signals. The other emerges from the frontal and temporal regions of the piriform cortex, involving the anterior insula, intermediate frontal sulcus, and parietal operculum. The results were confirmed in a replication cohort. Our results provide evidence that olfaction has unique early access to the central cortical networks via dual pathways.

Resting-State Network Plasticity Following Category Learning Depends on Sensory Modality

Learning new categories is fundamental to cognition, occurring in daily life through various sensory modalities. However, it is not well known how acquiring new categories can modulate the brain networks. Resting-state functional connectivity is an effective method for detecting short-term brain alterations induced by various modality-based learning experiences. Using fMRI, our study investigated the intricate link between novel category learning and brain network reorganization. Eighty-four adults participated in an object categorization experiment utilizing visual (n = 41, with 20 females and a mean age of 23.91 ± 3.11 years) or tactile (n = 43, with 21 females and a mean age of 24.57 ± 2.58 years) modalities. Resting-state networks (RSNs) were identified using independent component analysis across the group of participants, and their correlation with individual differences in object category learning across modalities was examined using dual regression. Our results reveal an increased functional connectivity of the frontoparietal network with the left superior frontal gyrus in visual category learning task and with the right superior occipital gyrus and the left middle temporal gyrus after tactile category learning. Moreover, the somatomotor network demonstrated an increased functional connectivity with the left parahippocampus exclusively after tactile category learning. These findings illuminate the neural mechanisms of novel category learning, emphasizing distinct brain networks' roles in diverse modalities. The dynamic nature of RSNs emphasizes the ongoing adaptability of the brain, which is essential for efficient novel object category learning. This research provides valuable insights into the dynamic interplay between sensory learning, brain plasticity, and network reorganization, advancing our understanding of cognitive processes across different modalities.

Connectome embedding in multidimensional graph spaces

Connectomes' topological organization can be quantified using graph theory. Here, we investigated brain networks in higher dimensional spaces defined by up to 10 graph theoretic nodal properties. These properties assign a score to nodes, reflecting their meaning in the network. Using 100 healthy unrelated subjects from the Human Connectome Project, we generated various connectomes (structural/functional, binary/weighted). We observed that nodal properties are correlated (i.e., they carry similar information) at whole-brain and subnetwork level. We conducted an exploratory machine learning analysis to test whether high-dimensional network information differs between sensory and association areas. Brain regions of sensory and association networks were classified with an 80-86% accuracy in a 10-dimensional (10D) space. We observed the largest gain in machine learning accuracy going from a 2D to 3D space, with a plateauing accuracy toward 10D space, and nonlinear Gaussian kernels outperformed linear kernels. Finally, we quantified the Euclidean distance between nodes in a 10D graph space. The multidimensional Euclidean distance was highest across subjects in the default mode network (in structural networks) and frontoparietal and temporal lobe areas (in functional networks). To conclude, we propose a new framework for quantifying network features in high-dimensional spaces that may reveal new network properties of the brain.

Interactions of transcranial magnetic stimulation with brain oscillations: a narrative review

Brain responses to transcranial magnetic stimulation (TMS) can be recorded with electroencephalography (EEG) and comprise TMS-evoked potentials and TMS-induced oscillations. Repetitive TMS may entrain endogenous brain oscillations. In turn, ongoing brain oscillations prior to the TMS pulse can influence the effects of the TMS pulse. These intricate TMS-EEG and EEG-TMS interactions are increasingly attracting the interest of researchers and clinicians. This review surveys the literature of TMS and its interactions with brain oscillations as measured by EEG in health and disease.

Shared and disorder-specific large-scale intrinsic and effective functional network connectivities in postpartum depression with and without anxiety

Postpartum depression and postpartum depression with anxiety, which are highly prevalent and debilitating disorders, become a growing public concern. The high overlap on the symptomatic and neurobiological levels led to ongoing debates about their diagnostic and neurobiological uniqueness. Delineating the shared and disorder-specific intrinsic functional connectivities and their causal interactions is fundamental to precision diagnosis and treatment. In this study, we recruited 138 participants including 45 postpartum depression, 31 postpartum depression comorbid with anxiety patients, and 62 healthy postnatal women with age ranging from 23 to 40 years. We combined independent component analysis, resting-state functional connectivity, and Granger causality analysis to reveal the abnormal intrinsic functional couplings and their causal interactions in postpartum depression and postpartum depression comorbid with anxiety from a large-scale brain network perspective. We found that they exhibited widespread abnormalities in intrinsic and effective functional network connectivities. Importantly, the intrinsic and effective functional network connectivities within or between the fronto-parietal network, default model network, ventral and dorsal attention network, sensorimotor network, and visual network, especially the functional imbalances between primary and association cortices could serve as effective neural markers to differentiate postpartum depression, postpartum depression comorbid with anxiety, and healthy controls. Our findings provide the initial evidence for shared and disorder-specific intrinsic and effective functional network connectivities for postpartum depression and postpartum depression comorbid with anxiety, which provide an underlying neuropathological basis for postpartum depression or postpartum depression comorbid with anxiety to facilitate precision diagnosis and therapy in future studies.

Exercise intervention on the brain structure and function of patients with mild cognitive impairment: systematic review based on magnetic resonance imaging studies

Objective

This systematic review evaluates the impact of exercise intervention in MCI patients and discusses the potential neural mechanisms.

Methods

A systematic search and screening of relevant literature was conducted in English and Chinese databases. Based on predefined keywords and criteria, 24 articles were assessed and analyzed.

Results

Structurally, a significant increase was observed in the hippocampal and gray matter volumes of MCI patients following exercise intervention, with a trend of improvement in cortical thickness and white matter integrity. Functionally, after the exercise intervention, there were significant changes in the local spontaneous brain activity levels, cerebral blood flow, and functional connectivity during rest and memory encoding and retrieval tasks in MCI patients.

Conclusion

Exercise may contribute to delaying neurodegenerative changes in brain structure and function in patients with MCI. However, the underlying neural mechanisms require further research.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023482419.

Recent trends in multiple metrics and multimodal analysis for neural activity and pupillometry

Recent studies focusing on neural activity captured by neuroimaging modalities have provided various metrics for elucidating the functional networks and dynamics of the entire brain. Functional magnetic resonance imaging (fMRI) can depict spatiotemporal functional neural networks and dynamic characteristics due to its excellent spatial resolution. However, its temporal resolution is limited. Neuroimaging modalities such as electroencephalography (EEG) and magnetoencephalography (MEG), which have higher temporal resolutions, are utilized for multi-temporal scale and multi-frequency-band analyzes. With this advantage, numerous EEG/MEG-bases studies have revealed the frequency-band specific functional networks involving dynamic functional connectivity and multiple temporal-scale time-series patterns of neural activity. In addition to analyzing neural data, the examination of behavioral data can unveil additional aspects of brain activity through unimodal and multimodal data analyzes performed using appropriate integration techniques. Among the behavioral data assessments, pupillometry can provide comprehensive spatial-temporal-specific features of neural activity. In this perspective, we summarize the recent progress in the development of metrics for analyzing neural data obtained from neuroimaging modalities such as fMRI, EEG, and MEG, as well as behavioral data, with a special focus on pupillometry data. First, we review the typical metrics of neural activity, emphasizing functional connectivity, complexity, dynamic functional connectivity, and dynamic state transitions of whole-brain activity. Second, we examine the metrics related to the time-series data of pupillary diameters and discuss the possibility of multimodal metrics that combine neural and pupillometry data. Finally, we discuss future perspectives on these multiple and multimodal metrics.

Duplicated Heschl's gyrus associations with phonological decoding

The reason(s) for why a complete duplication of the left hemisphere Heschl's gyrus (HG) has been observed in people with reading disability are unclear. This study was designed to replicate and advance understanding of the HG and phonological decoding association, as well as test competing hypotheses that this HG duplication association is specifically localized to the HG or could be due to co-occurring atypical development of other brain regions that support reading and language development. Participants were selected on the basis of having a duplicated left hemisphere HG (N = 96) or a single HG (N = 96) and matched according to age, sex, and research site in this multi-site study. Duplicated and single HG morphology specific templates were created to determine the extent to which HG sizes were related to phonological decoding within each HG morphology group. The duplicated HG group had significantly lower phonological decoding (F = 4.48, p = 0.04) but not verbal IQ (F = 1.39, p = 0.41) compared to the single HG group. In addition, larger HG were significantly associated with lower phonological decoding in the duplicated HG group, with effects driven by the size of the lateral HG after controlling for age, sex, research site, and handedness (ps < 0.05). Brain regions that exhibited structural covariance with HG did not clearly explain the HG and phonological decoding associations. Together, the results suggest that presence of a duplicated HG indicates some risk for lower phonological decoding ability compared to verbal IQ, but the reason(s) for this association remain unclear.

Motor impulsivity and spicy food craving: A mediation analysis of insula-based resting state functional connectivity

In China, the rate of spicy food consumption is rising, and chili pepper is among the most popular spicy foods consumed nationwide. However, little effort has been made to understand the mechanism behind spicy food craving. This exploratory study aimed to investigate differences in insula-based resting state functional connectivity (rsFC) between spicy food cravers and non-cravers, and the association between rsFC, impulsivity and spicy food craving. A group of extreme cravers (n = 49) and a group of age- and sex-matched non-cravers (n = 46) completed a resting-state fMRI scan, during which participants were instructed to keep their eyes closed, to not think of anything in particular, and to remain awake. Participants completed the Spicy Food Craving Questionnaire, Barratt Impulsiveness Scale, Sensation Seeking Scale and Positive and Negative Affect Schedule, and rated the frequency of spicy food intake. Results revealed increased insula-occipital lobe resting-state functional connectivity in individuals with spicy food cravings, and the positive correlations between insula-middle occipital gyrus rsFC, impulsivity and spicy food craving. Specifically, the insula-middle occipital gyrus rsFC strength mediated the relationship between the motor impulsivity and spicy food craving. It is hoped that our exploratory findings may shed new insights into the neural mechanisms of spicy food craving and motivate further exploration of spicy food craving in diverse contexts and cultures.

Catecholaminergic Modulation of Large-Scale Network Dynamics Is Tied to the Reconfiguration of Corticostriatal Connectivity

Large-scale brain network function is critical for healthy cognition, yet links between such network function, neurochemistry, and smaller-scale neurocircuitry are unclear. Here, we evaluated 59 healthy individuals using resting-state fMRI to determine how network-level temporal dynamics were impacted by two well-characterized pharmacotherapies targeting catecholamines: methylphenidate (20 mg) and haloperidol (2 mg)-administered via randomized, double-blind, placebo-controlled design. Network temporal dynamic changes were tested for links with drug-induced alterations in complex corticostriatal connections as this circuit is a primary site of action for both drugs. Methylphenidate increased time in the default mode network state (DMN p < 0.001) and dorsal attention network state (DAN p < 0.001) and reduced time in the frontoparietal network state (p < 0.01). Haloperidol increased time in a sensory motor-DMN state (p < 0.01). The magnitude of change in network dynamics induced by methylphenidate vs. placebo correlated with the magnitude of methylphenidate-induced rearrangement of complex corticostriatal connectivity (R = 0.32, p = 0.014). Haloperidol did not alter complex corticostriatal connectivity. Methylphenidate enhanced time in network states involved in internal and external attention (DMN and DAN, respectively), aligning with methylphenidate's established role in attention. Methylphenidate also significantly changed complex corticostriatal connectivity by altering the relative strength between multiple corticostriatal connections, indicating that methylphenidate may shift which corticostriatal connections are prioritized relative to others. Findings show that these corticostriatal circuit changes are linked with large-scale network temporal dynamics. Collectively, these findings provide a deeper understanding of large-scale network function, set a stage for mechanistic understanding of network engagement, and provide useful information to guide medication use based on network-level effects. Trial Registration: Registry name: ClinicalTrials.gov; URL: Brain Networks and Addiction Susceptibility-Full Text View-ClinicalTrials.gov; URL Plain text: https://classic.clinicaltrials.gov/ct2/show/NCT01924468; Identifier: NCT01924468.

Exploring the late maturation of an intrinsic episodic memory network: A resting-state fMRI study

Previous research suggests that episodic memory relies on functional neural networks,which are present even in the absence of an explicit task. The regions that integrate.these networks and the developmental changes in intrinsic functional connectivity.remain elusive. In the present study, we outlined an intrinsic episodic memory network.(iEMN) based on a systematic selection of functional connectivity studies, and.inspected network differences in resting-state fMRI between adolescents (13-17 years.old) and adults (23-27 years old) from the publicly available NKI-Rockland Sample.Through a review of brain regions commonly associated with episodic memory.networks, we identified a potential iEMN composed by 14 bilateral ROIs, distributed.across temporal, frontal and parietal lobes. Within this network, we found an increase.in resting-state connectivity from adolescents to adults between the right temporal pole.and two regions in the right lateral prefrontal cortex. We argue that the coordination of.these brain regions, connecting areas of semantic processing and areas of controlled.retrieval, arises as an important feature towards the full maturation of the episodic.memory system. The findings add to evidence suggesting that adolescence is a key.period in memory development and highlights the role of intrinsic functional.connectivity in such development.

Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI

Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great potential in medical and psychological diagnostics. The aim of the present study was to develop a machine learning tool for the detection of dyslexia in children based on the intrinsic connectivity patterns of different brain networks underlying perception and attention. Here, 117 children (8-12 years old; 58 females; 52 typical readers; TR and 65 children with dyslexia) completed cognitive and reading assessments and underwent 10 min of resting-state fMRI. Functional connectivity coefficients between 264 brain regions were used as features for machine learning. Different supervised algorithms were employed for classification of children with and without dyslexia. A classifier trained on dorsal attention network features exhibited the highest performance (accuracy .79, sensitivity .92, specificity .64). Auditory, visual, and fronto-parietal network-based classification showed intermediate accuracy levels (70-75%). These results highlight significant neurobiological differences in brain networks associated with visual attention between TR and children with dyslexia. Distinct neural integration patterns can differentiate dyslexia from typical development, which may be utilized in the future as a biomarker for the presence and/or severity of dyslexia.

Normative Neuroimaging Library: Designing a Comprehensive and Demographically Diverse Dataset of Healthy Controls to Support Traumatic Brain Injury Diagnostic and Therapeutic Development

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

Frontoparietal network integrity supports cognitive function in pre-symptomatic frontotemporal dementia: Multimodal analysis of brain function, structure, and perfusion

INTRODUCTION

Genetic mutation carriers of frontotemporal dementia can remain cognitively well despite neurodegeneration. A better understanding of brain structural, perfusion, and functional patterns in the pre-symptomatic stage could inform accurate staging and potential mechanisms.

METHODS

We included 207 pre-symptomatic genetic mutation carriers and 188 relatives without mutations. The gray matter volume, cerebral perfusion, and resting-state functional network maps were co-analyzed using linked independent component analysis (LICA). Multiple regression analysis was used to investigate the relationship of LICA components to genetic status and cognition.

RESULTS

Pre-symptomatic mutation carriers showed an age-related decrease in the left frontoparietal network integrity, while non-carriers did not. Executive functions of mutation carriers became dependent on the left frontoparietal network integrity in older age.

DISCUSSION

The frontoparietal network integrity of pre-symptomatic mutation carriers showed a distinctive relationship to age and cognition compared to non-carriers, suggesting a contribution of the network integrity to brain resilience.

HIGHLIGHTS

A multimodal analysis of structure, perfusion, and functional networks. The frontoparietal network integrity decreases with age in pre-symptomatic carriers only. Executive functions of pre-symptomatic carriers dissociated from non-carriers.

Histamine H3 receptor inverse agonists/antagonists influence intra-regional cortical activity and inter-regional synchronization during resting state: an exploratory cortex-wide imaging study in mice

The histaminergic system plays a key role in modulating learning and memory, wakefulness, and energy balance. Histamine H3 receptors constitutively inhibit the synthesis and release of histamine and other neurotransmitters. Therefore, H3 receptor inverse agonists/antagonists increase the synthesis and release of these neurotransmitters, enhancing cognitive functions, including memory consolidation and retrieval. Spontaneous neural activity across the cerebral cortex is essential for cognitive function, including memory consolidation. Abnormal spontaneous activity has, in fact, been associated with cognitive dysfunctions and psychiatric disorders. Given the cognitive improvement achieved with the use of H3 receptor inverse agonists/antagonists, we examined the effects of two inverse agonists/antagonists - thioperamide and pitolisant - on spontaneous cortical activity, using in vivo wide-field Ca2+ imaging. Changes in cortical activity, across multiple cortical regions and in inter-regional connectivity, from pre- to post-administration were evaluated using a linear support vector machine decoder. Thioperamide and pitolisant both modified the amplitude distribution of calcium events across multiple cortical regions, including a reduction in the frequency of low-amplitude calcium events in the somatosensory cortex. Graph theory analysis revealed increases in centrality measures in the somatosensory cortex with the use of both thioperamide and pitolisant, indicative of their importance in the organization of cortical networks. These findings indicate that H3 receptor inverse agonists/antagonists influence intra-regional cortical activity and inter-regional synchronization of activity in the cerebral cortex during the resting state.

Born Connected: Do Infants Already Have Adult-Like Multi-Scale Connectivity Networks?

The human brain undergoes remarkable development with the first six postnatal months witnessing the most dramatic structural and functional changes, making this period critical for in-depth research. rsfMRI studies have identified intrinsic connectivity networks (ICNs), including the default mode network, in infants. Although early formation of these networks has been suggested, the specific identification and number of ICNs reported in infants vary widely, leading to inconclusive findings. In adults, ICNs have provided valuable insights into brain function, spanning various mental states and disorders. A recent study analyzed data from over 100,000 subjects and generated a template of 105 multi-scale ICNs enhancing replicability and generalizability across studies. Yet, the presence of these ICNs in infants has not been investigated. This study addresses this significant gap by evaluating the presence of these multi-scale ICNs in infants, offering critical insight into the early stages of brain development and establishing a baseline for longitudinal studies. To accomplish this goal, we employ two sets of analyses. First, we employ a fully data-driven approach to investigate the presence of these ICNs from infant data itself. Towards this aim, we also introduce burst independent component analysis (bICA), which provides reliable and unbiased network identification. The results reveal the presence of these multi-scale ICNs in infants, showing a high correlation with the template (rho > 0.5), highlighting the potential for longitudinal continuity in such studies. We next demonstrate that reference-informed ICA-based techniques can reliably estimate these ICNs in infants, highlighting the feasibility of leveraging the NeuroMark framework for robust brain network extraction. This approach not only enhances cross-study comparisons across lifespans but also facilitates the study of brain changes across different age ranges. In summary, our study highlights the novel discovery that the infant brain already possesses ICNs that are widely observed in older cohorts.

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.72s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (~30s), but larger windows (~88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

Schizophrenia is a chronic brain disorder associated with widespread alterations in functional brain connectivity. Although data-driven approaches such as independent component analysis are often used to study how schizophrenia impacts linearly connected networks, alterations within the underlying nonlinear functional connectivity structure remain largely unknown. Here we report the analysis of networks from explicitly nonlinear functional magnetic resonance imaging connectivity in a case-control dataset. We found systematic spatial variation, with higher nonlinear weight within core regions, suggesting that linear analyses underestimate functional connectivity within network centers. We also found that a unique nonlinear network incorporating default-mode, cingulo-opercular and central executive regions exhibits hypoconnectivity in schizophrenia, indicating that typically hidden connectivity patterns may reflect inefficient network integration in psychosis. Moreover, nonlinear networks including those previously implicated in auditory, linguistic and self-referential cognition exhibit heightened statistical sensitivity to schizophrenia diagnosis, collectively underscoring the potential of our methodology to resolve complex brain phenomena and transform clinical connectivity analysis.

Hyperscanning: from inter-brain coupling to causality

In hyperscanning studies, participants perform a joint task while their brain activation is simultaneously recorded. Evidence of inter-brain coupling is examined, in these studies, as a predictor of behavioral change. While the field of hyperscanning has made significant strides in unraveling the associations between inter-brain coupling and changes in social interactions, drawing causal conclusions between brain and behavior remains challenging. This difficulty arises from factors like the inherently different timescales of behavioral responses and measured cerebral activity, as well as the predominant focus of existing methods on associations rather than causality. Specifically, a question remains as to whether inter-brain coupling between specific brain regions leads to changes in behavioral synchrony, or vice-versa. We propose two novel approaches to addressing this question. The first method involves using dyadic neurofeedback, wherein instances of inter-brain coupling are directly reinforced. Such a system could examine if continuous changes of inter-brain coupling are the result of deliberate mutual attempts to synchronize. The second method employs statistical approaches, including Granger causality and Structural Equation Modeling (SEM). Granger causality assesses the predictive influence of one time series on another, enabling the identification of directional neural interactions that drive behavior. SEM allows for detailed modeling of both direct and indirect effects of inter-brain coupling on behavior. We provide an example of data analysis with the SEM approach, discuss the advantages and limitations of each approach and posit that applying these approaches could provide significant insights into how inter-brain coupling supports crucial processes that occur in social interactions.

Oculomotor functional connectivity associated with motor sequence learning

Acquisition of learned motor sequences involves saccades directed toward the goal to gather visual information prior to reaching. While goal-directed actions involve both eye and hand movements, the role of brain areas controlling saccades during motor sequence learning is still unclear. This study aimed to determine whether resting-state functional connectivity of oculomotor regions is associated with behavioral changes resulting from motor sequence learning. We investigated connectivity between oculomotor control regions and candidate regions involved in oculomotor control and motor sequence learning. Twenty adults had brain scans before 3 days of motor task practice and after a 24-hour retention test, which was used to assess sequence-specific learning. During testing, both saccades and reaches were tracked. Stronger connectivity in multiple oculomotor regions prior to motor task practice correlated with greater sequence-specific learning for both saccades and reaches. A more negative connectivity change involving oculomotor regions from pre- to post-training correlated with greater sequence-specific learning for both saccades and reaches. Overall, oculomotor functional connectivity was associated with the magnitude of behavioral change resulting from motor sequence learning, providing insight into the function of the oculomotor system during motor sequence learning.

Functional connectivity of sensorimotor network before and after surgery in the supplementary motor area

After resective glioma surgery in the Supplementary Motor Area (SMA), patients often experience a transient disturbance of the ability to initiate speech and voluntary motor actions, known as the SMA syndrome (SMAS). It has been proposed that enhanced interhemispheric functional connectivity (FC) within the sensorimotor system may serve as a potential mechanism for recovery, enabling the non-resected SMA to assume the function of the resected region. The purpose of the present study was to investigate the extent to which changes in FC can be observed in patients after resolution of the SMAS. Eight patients underwent resection of left SMA due to suspected gliomas, resulting in various levels of the SMA syndrome. Resting-state functional MR images were acquired prior to the surgery and after resolution of the syndrome. At the group level we found an increased connectivity between the unaffected (right) SMA and the primary motor cortex on the same side following surgery. However, no significant increase in interhemispheric connectivity was observed. These findings challenge the prevailing notion that increased interhemispheric FC serves as the only mechanism underlying recovery from SMA syndrome and suggest the presence of one or more alternative mechanisms.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Delineating Region-Specific contributions and connectivity patterns for semantic association and categorization through ROI and Granger causality analysis

The neural mechanisms supporting semantic association and categorization are examined in this study. Semantic association involves linking concepts through shared themes, events, or scenes, while semantic categorization organizes meanings hierarchically based on defining features. Twenty-three adults participated in an fMRI study performing categorization and association judgment tasks. Results showed stronger activation in the inferior frontal gyrus during association and marginally weaker activation in the posterior middle temporal gyrus (pMTG) during categorization. Granger causality analysis revealed bottom-up connectivity from the visual cortex to the hippocampus during semantic association, whereas semantic categorization exhibited strong reciprocal connections between the pMTG and frontal semantic control regions, together with information flow from the visual association area and hippocampus to the pars triangularis. We propose that demands on semantic retrieval, precision of semantic representation, perceptual experiences and world knowledge result in observable differences between these two semantic relations.

Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction

Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.

Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network

Resting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz.

Exploring visuospatial function neuroplasticity in elite speed skaters: a resting-state fMRI independent component analysis

BACKGROUND

Limited research has been conducted on the neural mechanisms of visuospatial attention in closed skill sports. This research aimed to delve into the unique visuospatial attention abilities of skaters and elucidate the underlying neural mechanisms.

METHODS

This cross-sectional study employed an expert-novice paradigm, applying a purely data-driven approach to analyze and compare the resting-state networks (RSNs) associated with visuospatial attention in 15 elite skaters and 15 control subjects.

RESULTS

From the 38 components identified by independent component analysis (ICA) algorithm, five RSNs were selected, including the dorsal attention network (DAN), left and right fronto-parietal network (FPN), somatomotor network (SMN) and visual network (VIS). Elite skaters exhibited heightened functional connectivity (FC) in the right angular gyrus and left precuneus within DAN, left fusiform gyrus within left FPN, right primary motor cortex within right FPN, left supplementary motor area within SMN, and right primary visual cortex within VIS compared to the control group. Conversely, skaters demonstrated diminished FC in the bilateral superior temporal gyrus within DAN and right prefrontal cortex within the right FPN.

CONCLUSIONS

Statistical results demonstrated significant differences in RSNs related to visuospatial functions in a wide range of brain regions between elite skaters and controls. We further speculate that these variances could be attributable to alterations in visuospatial abilities resulting from years of devoted skating training. The findings of this study offer novel perspectives on the neural reorganization linked to motor training, contributing to an enriched comprehension of the neuroplasticity changes inherent in prolonged engagement in motor skill development.

Temporal dissociation between local and global functional adaptations of the maternal brain to childbirth: a longitudinal assessment

The maternal brain undergoes significant reorganization during birth and the postpartum period. However, the temporal dynamics of these changes remain unclear. Using resting-state functional magnetic resonance imaging, we report on local and global brain function alterations in 75 mothers in their first postpartum week, compared to 23 nulliparous women. In a subsample followed longitudinally for the next six months, we observed a temporal and spatial dissociation between changes observed at baseline (cluster mass permutation: pFWE < 0.05). Local activity and connectivity changes in widespread neocortical regions persisted throughout the studied time period (ANCOVAs vs. controls: pFDR < 0.05), with preliminary evidence linking these alterations to behavioral and psychological adaptations (interaction effect with postpartum time: uncorrected p < 0.05). In contrast, the initially reduced whole-brain connectivity of putamen-centered subcortical areas returned to control levels within six to nine weeks postpartum (linear and quadratic mixed linear models: pFDR < 0.05). The whole-brain spatial colocalization with hormone receptor distributions (Spearman correlations: pFDR < 0.05) and preliminary blood hormone associations (interaction effect with postpartum time: uncorrected p < 0.05) suggested that the postpartum restoration of progesterone levels may underlie this rapid normalization. These observations enhance our understanding of healthy maternal brain function, contributing to the identification of potential markers for pathological postpartum adaptation processes, which in turn could underlie postpartum psychiatric disorders.

Shedding Light on the Aftermath: Childhood Maltreatment's Role in Modifying the Association Between Recent Life Stress and Resting-State Network Connectivity

Childhood maltreatment has been demonstrated to impact brain development. However, whether childhood maltreatment can influence the effects of recent stress on brain networks remains unclear. This study aimed to investigate whether childhood maltreatment moderates the longitudinal relationship between recent life stress and within- and between-network connectivity in key brain networks, including the anterior salience (ASN), central executive (CEN), default mode (DMN), and emotional regulation network (ERN). A cohort of 172 individuals from the Neuroscience in Psychiatry Network (NSPN) underwent MRI scans at two specific time points and undertook evaluations of childhood maltreatment and recent life stress. The results showed that childhood abuse moderated the association of recent life stress with the within-network connectivity of ASN and ERN but not DMN and CEN. Furthermore, recent life stress significantly interacted with childhood abuse to be associated with the between-network connectivity of ASN-DMN, ASN-CEN, ASN-ERN, DMN-ERN and CEN-ERN. Overall, among youth exposed to higher degrees of childhood abuse, greater recent life stress was longitudinally associated with increased network connectivity. Understanding these interactions can provide valuable insights for developing prevention strategies and interventions aimed at mitigating the lasting impact of childhood maltreatment on brain development and overall well-being.

Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis

BACKGROUND

Although gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM.

METHODS

In this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback-Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs.

RESULTS

Compared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively.

CONCLUSION

Patients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Intrinsic functional networks for distinct sources of error in visual working memory

Visual working memory (VWM) is a core cognitive function wherein visual information is stored and manipulated over short periods. Response errors in VWM tasks arise from the imprecise memory of target items, swaps between targets and nontargets, and random guesses. However, it remains unclear whether these types of errors are underpinned by distinct neural networks. To answer this question, we recruited 80 healthy adults to perform delayed estimation tasks and acquired their resting-state functional magnetic resonance imaging scans. The tasks required participants to reproduce the memorized visual feature along continuous scales, which, combined with mixture distribution modeling, allowed us to estimate the measures of memory precision, swap errors, and random guesses. Intrinsic functional connectivity within and between different networks, identified using a hierarchical clustering approach, was estimated for each participant. Our analyses revealed that higher memory precision was associated with increased connectivity within a frontal-opercular network, as well as between the dorsal attention network and an angular-gyrus-cerebellar network. We also found that coupling between the frontoparietal control network and the cingulo-opercular network contributes to both memory precision and random guesses. Our findings demonstrate that distinct sources of variability in VWM performance are underpinned by different yet partially overlapping intrinsic functional networks.

An improved spectral clustering method for accurate detection of brain resting-state networks

This paper proposes a data-driven analysis method to accurately partition large-scale resting-state functional brain networks from fMRI data. The method is based on a spectral clustering algorithm and combines eigenvector direction selection with Pearson correlation clustering in the spectral space. The method is an improvement on available spectral clustering methods, capable of robustly identifying active brain networks consistent with those from model-driven methods at different noise levels, even at the noise level of real fMRI data.

EEG resting state alpha dynamics predict an individual's vulnerability to auditory hallucinations

Task-free brain activity exhibits spontaneous fluctuations between functional states, characterized by synchronized activation patterns in distributed resting-state (RS) brain networks. The temporal dynamics of the networks' electrophysiological signatures reflect individual variations in brain activity and connectivity linked to mental states and cognitive functions and can predict or monitor vulnerability to develop psychiatric or neurological disorders. In particular, RS alpha fluctuations modulate perceptual sensitivity, attentional shifts, and cognitive control, and could therefore reflect a neural correlate of increased vulnerability to sensory distortions, including the proneness to hallucinatory experiences. We recorded 5 min of RS EEG from 33 non-clinical individuals varying in hallucination proneness (HP) to investigate links between task-free alpha dynamics and vulnerability to hallucinations. To this end, we used a dynamic brain state allocation method to identify five recurrent alpha states together with their spatiotemporal dynamics and most active brain areas through source reconstruction. The dynamical features of a state marked by activation in somatosensory, auditory, and posterior default-mode network areas predicted auditory and auditory-verbal HP, but not general HP, such that individuals with higher vulnerability to auditory hallucinations spent more time in this state. The temporal dynamics of spontaneous alpha activity might reflect individual differences in attention to internally generated sensory events and altered auditory perceptual sensitivity. Altered RS alpha dynamics could therefore instantiate a neural marker of increased vulnerability to auditory hallucinations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-024-10093-1.

Not So Transient?: A Narrative Review on Cognitive Impairment After Transient Ischemic Attack

Transient ischemic attack (TIA) is traditionally viewed as a self-resolving episode of neurological change without persistent impairments and without evidence of acute brain injury on neuroimaging. However, emerging evidence suggests that TIA may be associated with lingering cognitive dysfunction. Cognitive impairment is a prevalent and disabling sequela of ischemic stroke, but the clinical relevance of this phenomenon after TIA is less commonly recognized. We performed a literature search of observational studies of cognitive function after TIA. There is a consistent body of literature suggesting that rates of cognitive impairment following TIA are higher than healthy controls, but the studies included here are limited by heterogeneity in design and analysis methods. We go on to summarize recent literature on proposed pathophysiological mechanisms underlying the development of cognitive impairment following TIA and finally suggest future directions for further research in this field.

Alterations in corpus callosum subregions morphology and functional connectivity in patients with adult-onset hypothyroidism

BACKGROUND

Magnetic resonance imaging (MRI) brain abnormalities have been reported in the corpus callosum (CC) of patients with adult-onset hypothyroidism. However, no study has directly compared CC-specific morphological or functional alterations among subclinical hypothyroidism (SCH), overt hypothyroidism (OH), and healthy controls (HC). Moreover, the association of CC alterations with cognition and emotion is not well understood.

METHODS

Demographic data, clinical variables, neuropsychological scores, and MRI data of 152 participants (60 SCH, 37 OH, and 55 HC) were collected. This study investigated the clinical performance, morphological and functional changes of CC subregions across three groups. Moreover, a correlation analysis was performed to explore potential relationships between these factors.

RESULTS

Compared to HC, SCH and OH groups exhibited lower cognitive scores and higher depressive/anxious scores. Notably, rostrum and rostral body volume of CC was larger in the SCH group. Functional connectivity between rostral body, anterior midbody and the right precentral and dorsolateral superior frontal gyrus were increased in the SCH group. In contrast, the SCH and OH groups exhibited a decline in functional connectivity between splenium and the right angular gyrus. Within the SCH group, rostrum volume demonstrated a negative correlation with Montreal Cognitive Assessment and visuospatial/executive scores, while displaying a positive correlation with 24-item Hamilton Depression Rating Scale scores. In the OH group, rostral body volume exhibited a negative correlation with serum thyroid stimulating hormone levels, while a positive correlation with serum total thyroxine and free thyroxine levels.

CONCLUSIONS

This study suggests that patients with different stages of adult-onset hypothyroidism may exhibit different patterns of CC abnormalities. These findings offer new insights into the neuropathophysiological mechanisms in hypothyroidism.

D-MHGCN: An End-to-End Individual Behavioral Prediction Model Using Dual Multi-Hop Graph Convolutional Network

Predicting individual behavior is a crucial area of research in neuroscience. Graph Neural Networks (GNNs), as powerful tools for extracting graph-structured features, are increasingly being utilized in various functional connectivity (FC) based behavioral prediction tasks. However, current predictive models primarily focus on enhancing GNNs' ability to extract features from FC networks while neglecting the importance of upstream individual network construction quality. This oversight results in constructed functional networks that fail to adequately represent individual behavioral capacity, thereby affecting the subsequent prediction accuracy. To address this issue, we proposed a new GNN-based behavioral prediction framework, named Dual Multi-Hop Graph Convolutional Network (D-MHGCN). Through the joint training of two GCNs, this framework integrates individual functional network construction and behavioral prediction into a unified optimization model. It allows the model to dynamically adjust the individual functional cortical parcellation according to the downstream tasks, thus creating task-aware, individual-specific FCNs that largely enhance its ability to predict behavior scores. Additionally, we employed multi-hop graph convolution layers instead of traditional single-hop methods in GCN to capture complex hierarchical connectivity patterns in brain networks. Our experimental evaluations, conducted on the large, public Human Connectome Project dataset, demonstrate that our proposed method outperforms existing methods in various behavioral prediction tasks. Moreover, it produces more functionally homogeneous cortical parcellation, showcasing its practical utility and effectiveness. Our work not only enhances the accuracy of individual behavioral prediction but also provides deeper insights into the neural mechanisms underlying individual differences in behavior.

The "limbic network," comprising orbitofrontal and anterior temporal cortex, is part of an extended default network: Evidence from multi-echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) investigations have provided a view of the default network (DN) as composed of a specific set of frontal, parietal, and temporal cortical regions. This spatial topography is typically defined with reference to an influential network parcellation scheme that designated the DN as one of seven large-scale networks (Yeo et al., 2011). However, the precise functional organization of the DN is still under debate, with studies arguing for varying subnetwork configurations and the inclusion of subcortical regions. In this vein, the so-called limbic network-defined as a distinct large-scale network comprising the bilateral temporal poles, ventral anterior temporal lobes, and orbitofrontal cortex-is of particular interest. A large multi-modal and multi-species literature on the anatomical, functional, and cognitive properties of these regions suggests a close relationship to the DN. Notably, these regions have poor signal quality with conventional fMRI acquisition, likely obscuring their network affiliation in most studies. Here, we leverage a multi-echo fMRI dataset with high temporal signal-to-noise and whole-brain coverage, including orbitofrontal and anterior temporal regions, to examine the large-scale network resting-state functional connectivity of these regions and assess their associations with the DN. Consistent with our hypotheses, our results support the inclusion of the majority of the orbitofrontal and anterior temporal cortex as part of the DN and reveal significant heterogeneity in their functional connectivity. We observed that left-lateralized regions within the temporal poles and ventral anterior temporal lobes, as well as medial orbitofrontal regions, exhibited the greatest resting-state functional connectivity with the DN, with heterogeneity across DN subnetworks. Overall, our findings suggest that, rather than being a functionally distinct network, the orbitofrontal and anterior temporal regions comprise part of a larger, extended default network.

Dynamical structure-function correlations provide robust and generalizable signatures of consciousness in humans

Resting-state functional magnetic resonance imaging evolves through a repertoire of functional connectivity patterns which might reflect ongoing cognition, as well as the contents of conscious awareness. We investigated whether the dynamic exploration of these states can provide robust and generalizable markers for the state of consciousness in human participants, across loss of consciousness induced by general anaesthesia or slow wave sleep. By clustering transient states of functional connectivity, we demonstrated that brain activity during unconsciousness is dominated by a recurrent pattern primarily mediated by structural connectivity and with a reduced capacity to transition to other patterns. Our results provide evidence supporting the pronounced differences between conscious and unconscious brain states in terms of whole-brain dynamics; in particular, the maintenance of rich brain dynamics measured by entropy is a critical aspect of conscious awareness. Collectively, our results may have significant implications for our understanding of consciousness and the neural basis of human awareness, as well as for the discovery of robust signatures of consciousness that are generalizable among different brain conditions.

Contracted functional connectivity profiles in autism

OBJECTIVE

Autism spectrum disorder (ASD) is a neurodevelopmental condition that is associated with atypical brain network organization, with prior work suggesting differential connectivity alterations with respect to functional connection length. Here, we tested whether functional connectopathy in ASD specifically relates to disruptions in long- relative to short-range functional connections. Our approach combined functional connectomics with geodesic distance mapping, and we studied associations to macroscale networks, microarchitectural patterns, as well as socio-demographic and clinical phenotypes.

METHODS

We studied 211 males from three sites of the ABIDE-I dataset comprising 103 participants with an ASD diagnosis (mean ± SD age = 20.8 ± 8.1 years) and 108 neurotypical controls (NT, 19.2 ± 7.2 years). For each participant, we computed cortex-wide connectivity distance (CD) measures by combining geodesic distance mapping with resting-state functional connectivity profiling. We compared CD between ASD and NT participants using surface-based linear models, and studied associations with age, symptom severity, and intelligence scores. We contextualized CD alterations relative to canonical networks and explored spatial associations with functional and microstructural cortical gradients as well as cytoarchitectonic cortical types.

RESULTS

Compared to NT, ASD participants presented with widespread reductions in CD, generally indicating shorter average connection length and thus suggesting reduced long-range connectivity but increased short-range connections. Peak reductions were localized in transmodal systems (i.e., heteromodal and paralimbic regions in the prefrontal, temporal, and parietal and temporo-parieto-occipital cortex), and effect sizes correlated with the sensory-transmodal gradient of brain function. ASD-related CD reductions appeared consistent across inter-individual differences in age and symptom severity, and we observed a positive correlation of CD to IQ scores.

LIMITATIONS

Despite rigorous harmonization across the three different acquisition sites, heterogeneity in autism poses a potential limitation to the generalizability of our results. Additionally, we focussed male participants, warranting future studies in more balanced cohorts.

CONCLUSIONS

Our study showed reductions in CD as a relatively stable imaging phenotype of ASD that preferentially impacted paralimbic and heteromodal association systems. CD reductions in ASD corroborate previous reports of ASD-related imbalance between short-range overconnectivity and long-range underconnectivity.

Early Family Experiences and Neural Activity in Rural Pakistani Children: The Differential Role of Gender

Adversity within low- and middle-income countries (LMICs) poses severe threats to neurocognitive development, which can be partially mitigated by high-quality early family experiences. Specifically, maternal scaffolding and home stimulation can buffer cognitive development in LMIC, possibly by protecting underlying neural functioning. However, the association between family experiences and neural activity remains largely unexplored in LMIC contexts. This study explored the relation of early family experiences to later cognitive skills and absolute gamma power (21-45 Hz), a neural marker linked to higher-order cognitive skills. Drawing data from the PEDS trial, a longitudinal study in rural Pakistan, we examined maternal scaffolding at 24 months and home stimulation quality at 18 months as predictors of verbal IQ, executive functions, and absolute gamma at 48 months for 105 mother-child dyads (52 girls). Maternal scaffolding interacted with gender to predict absolute gamma power, such that higher maternal scaffolding was related to higher gamma more strongly for girls. Maternal scaffolding also interacted with absolute gamma to predict executive functions, such that higher gamma was related to better executive functions only when maternal scaffolding was average to high. Individual differences in early family experiences may partially buffer the neural underpinnings of cognitive skills from adversity in LMIC.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Smoking affects global and regional brain entropy in depression patients regardless of depression: Preliminary findings

OBJECTIVE

This study examines the effect of smoking on global and regional brain entropy in patients with Major Depressive Disorder (MDD), aiming to elucidate the relationship between smoking habits and brain network complexity in depression.

METHODS

The study enrolled 24 MDD patients, divided into smokers and non-smokers, from Alanya Alaaddin Keykubat University and Istanbul Medipol University. Resting-state fMRI data were acquired and processed. The complexity of neuronal activity was assessed using dispersion entropy, with statistical significance determined by a suite of tests including Kolmogorov-Smirnov, Student's t-test, and Mann-Whitney U test.

RESULTS

The smoking cohort exhibited higher global brain entropy compared to the non-smoking group (p = 0.033), with significant differences in various brain networks, indicating that smoking may alter global brain activity and network dynamics in individuals with MDD.

CONCLUSION

The study provides evidence that smoking is associated with increased brain entropy in MDD patients, suggesting that chronic smoking may influence cognitive and emotional networks. This underscores the importance of considering smoking history in the treatment and prognosis of MDD. The findings call for further research to understand the mechanistic links between smoking, brain entropy, and depression.

Compressed cerebro-cerebellar functional gradients in children and adolescents with attention-deficit/hyperactivity disorder

Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.

Alterations of resting-state network dynamics in Alzheimer's disease based on leading eigenvector dynamics analysis

Alzheimer's disease (AD) is a neurodegenerative disease, and mild cognitive impairment (MCI) is considered a transitional stage between healthy aging and dementia. Early detection of MCI can help slow down the progression of AD. At present, there are few studies exploring the characteristics of abnormal dynamic brain activity in AD. This article uses a method called leading eigenvector dynamics analysis (LEiDA) to study resting-state functional magnetic resonance imaging (rs-fMRI) data of AD, MCI, and cognitively normal (CN) participants. By identifying repetitive states of phase coherence, intergroup differences in brain dynamic activity indicators are examined, and the neurobehavioral scales were used to assess the relationship between abnormal dynamic activities and cognitive function. The results showed that in the indicators of occurrence probability and lifetime, the globally synchronized state of the patient group decreased. The activity state of the limbic regions significantly detected the difference between AD and the other two groups. Compared to CN, AD and MCI have varying degrees of increase in default and visual region activity states. In addition, in the analysis related to the cognitive scales, it was found that individuals with poorer cognitive abilities were less active in the globally synchronized state and more active in limbic region activity state and visual region activity state. Taken together, these findings reveal abnormal dynamic activity of resting-state networks in patients with AD and MCI, provide new insights into the dynamic analysis of brain networks, and contribute to a deeper understanding of abnormal spatial dynamic patterns in AD patients.NEW & NOTEWORTHY Alzheimer's disease (AD) is a neurodegenerative disease, but few studies have explored the characteristics of abnormal dynamic brain activity in AD patients. Here, our report reveals the abnormal dynamic activity of the patients' resting-state network, providing new insights into the dynamic analysis of brain networks and helping to gain a deeper understanding of the abnormal spatial dynamic patterns in AD patients.

Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain's network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.

Neural basis responsible for effect of grit on procrastination: The interaction between the self-regulation and motivation neural pathways

Procrastination has a detrimental impact on academic performance, health, and subjective well-being. Previous studies indicated that grit was negatively related to procrastination. However, the underlying neural basis of this relationship remains unclear. To address this issue, we utilized voxel-based morphometry (VBM) and resting-state functional connectivity (RSFC) analysis to identify the neural substrates of how is grit linked to procrastination. Behavioral results showed that procrastination was negatively associated with grit. VBM analysis revealed that gray matter volume (GMV) in the left precuneus was positively associated with the consistency of interest (CI), a subcomponent of grit, while the right medial orbital frontal cortex (mOFC) was positively correlated with the perseverance of effort (PE), another subcomponent of grit. Moreover, the RSFC analysis indicated that both precuneus-medial superior frontal gyrus (mSFG) and precuneus-insula connectivity were positively related to CI, while the functional coupling of right mOFC with left anterior cingulate cortex (ACC) was positively related to PE. Importantly, the structural equation modeling (SEM) results were well suited for the influence of grit on procrastination via both self-regulation (mOFC-ACC) and motivation pathways (precuneus-mSFG, precuneus-insula). Together, these findings imply that self-regulation and motivation could be two neural circuits underlying the impact of grit on procrastination.

Specific static and dynamic functional network connectivity changes in thyroid-associated ophthalmopathy and it predictive values using machine learning

Background

Thyroid-associated ophthalmopathy (TAO) is a prevalent autoimmune disease characterized by ocular symptoms like eyelid retraction and exophthalmos. Prior neuroimaging studies have revealed structural and functional brain abnormalities in TAO patients, along with central nervous system symptoms such as cognitive deficits. Nonetheless, the changes in the static and dynamic functional network connectivity of the brain in TAO patients are currently unknown. This study delved into the modifications in static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) among thyroid-associated ophthalmopathy patients using independent component analysis (ICA).

Methods

Thirty-two patients diagnosed with thyroid-associated ophthalmopathy and 30 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. ICA method was utilized to extract the sFNC and dFNC changes of both groups.

Results

In comparison to the HC group, the TAO group exhibited significantly increased intra-network functional connectivity (FC) in the right inferior temporal gyrus of the executive control network (ECN) and the visual network (VN), along with significantly decreased intra-network FC in the dorsal attentional network (DAN), the default mode network (DMN), and the left middle cingulum of the ECN. On the other hand, FNC analysis revealed substantially reduced connectivity intra- VN and inter- cerebellum network (CN) and high-level cognitive networks (DAN, DMN, and ECN) in the TAO group compared to the HC group. Regarding dFNC, TAO patients displayed abnormal connectivity across all five states, characterized by notably reduced intra-VN connectivity and CN connectivity with high-level cognitive networks (DAN, DMN, and ECN), alongside compensatory increased connectivity between DMN and low-level perceptual networks (VN and basal ganglia network). No significant differences were observed between the two groups for the three dynamic temporal metrics. Furthermore, excluding the classification outcomes of FC within VN (with an accuracy of 51.61% and area under the curve of 0.35208), the FC-based support vector machine (SVM) model demonstrated improved performance in distinguishing between TAO and HC, achieving accuracies ranging from 69.35 to 77.42% and areas under the curve from 0.68229 to 0.81667. The FNC-based SVM classification yielded an accuracy of 61.29% and an area under the curve of 0.57292.

Conclusion

In summary, our study revealed that significant alterations in the visual network and high-level cognitive networks. These discoveries contribute to our understanding of the neural mechanisms in individuals with TAO, offering a valuable target for exploring future central nervous system changes in thyroid-associated eye diseases.