Implicit motor learning involves the acquisition and consolidation of motor skills without conscious awareness, influenced by various factors. Punishment and reward have been identified as significant modulators during training, impacting skill acquisition differently. Additionally, the role of a second declarative task in offline consolidation has been explored, affecting both stabilization and enhancement processes during wake and sleep periods. However, how valanced feedback and learning a secondary declarative task can influence the learning and consolidation of implicit motor learning has not been explored. This study investigates whether receiving monetary feedback during motor sequence learning influences consolidation when declarative knowledge about the task is disrupted by a second word-list task. Participants' skill levels were assessed during training, immediately after training, 15 min post-training (after performing the second task), and 24 h later after night sleep. Concurrently, brain synchrony was measured using electroencephalography (EEG) recording. Results indicate that monetary punishment leads to early enhancement and higher performance after the second task compared to reward and control groups. However, after 24 h, no significant enhancement was observed in any group, with differences between groups diminishing. EEG analysis revealed distinct brain subnetworks across alpha, beta, and unexpectedly delta network which traditionally associated with sleep-dependent consolidation. These findings shed light on the complex interplay between valanced feedback learning, declarative memory disruption, and offline consolidation in implicit motor learning, highlighting the dynamic nature of skill acquisition and retention, offering potential implications for targeted interventions and future research directions.

In contrast to blood-oxygenation level-dependent (BOLD) functional MRI (fMRI), which relies on changes in blood flow and oxygenation levels to infer brain activity, diffusion fMRI (DfMRI) investigates brain dynamics by monitoring alterations in the apparent diffusion coefficient (ADC) of water. These ADC changes may arise from fluctuations in neuronal morphology, providing a distinctive perspective on neural activity. The potential of ADC as an fMRI contrast (ADC-fMRI) lies in its capacity to reveal neural activity independently of neurovascular coupling, thus yielding complementary insights into brain function. To demonstrate the specificity and value of ADC-fMRI, both ADC- and BOLD-fMRI data were collected at 3 T in human subjects during visual stimulation and motor tasks. The first aim of this study was to identify an acquisition design for ADC that minimises BOLD contributions. By examining the timings in responses, we report that ADC 0/1 timeseries (acquired with b values of 0 and 1 ms/ ) exhibit residual vascular contamination, while ADC 0.2/1 timeseries (with b values of 0.2 and 1 ms/ ) show minimal BOLD influence and higher sensitivity to neuromorphological coupling. Second, a general linear model was employed to identify activation clusters for ADC 0.2/1 and BOLD, from which the average ADC and BOLD responses were calculated. The negative ADC response exhibited a significantly reduced delay relative to the task onset and offset as compared to BOLD. This early onset further supports the notion that ADC is sensitive to neuromorphological rather than neurovascular coupling. Remarkably, in the group-level analysis, positive BOLD activation clusters were detected in the visual and motor cortices, while the negative ADC clusters mainly highlighted pathways in white matter connected to the motor cortex. In the averaged individual level analysis, negative ADC activation clusters were also present in the visual cortex. This finding confirmed the reliability of negative ADC as an indicator of brain function, even in regions with lower vascularisation such as white matter. Finally, we established that ADC-fMRI time courses yield the expected functional organisation of the visual system, including both grey and white matter regions of interest. Functional connectivity matrices were used to perform hierarchical clustering of brain regions, where ADC-fMRI successfully reproduced the expected structure of the dorsal and ventral visual pathways. This organisation was not replicated with the b = 0.2 ms/ diffusion-weighted time courses, which can be seen as a proxy for BOLD (via T-weighting). These findings underscore the robustness of ADC time courses in functional MRI studies, offering complementary insights into BOLD-fMRI regarding brain function and connectivity patterns.

Language control processes allow for the flexible manipulation and access to context-appropriate verbal representations. Functional magnetic resonance imaging (fMRI) studies have localized the brain regions involved in language control processes usually by comparing high vs. low lexical-semantic control conditions during verbal tasks. Yet, the spectro-temporal dynamics of associated brain processes remain unexplored, preventing a proper understanding of the neural bases of language control mechanisms. To do so, we recorded functional brain activity using magnetoencephalography (MEG) and fMRI, while 30 healthy participants performed a silent verb generation (VGEN) and a picture naming (PN) task upon confrontation with pictures requiring low or high lexical-semantic control processes. fMRI confirmed the association between stronger language control processes and increased left inferior frontal gyrus (IFG) perfusion, while MEG revealed these controlled mechanisms to be associated with a specific sequence of early (< 500 ms) and late (> 500 ms) beta-band (de)synchronization processes within fronto-temporo-parietal areas. Particularly, beta-band modulations of event-related (de)synchronization mechanisms were first observed in the right IFG, followed by bilateral IFG and temporo-parietal brain regions. Altogether, these results suggest that beyond a specific recruitment of inferior frontal brain regions, language control mechanisms rely on a complex temporal sequence of beta-band oscillatory mechanisms over antero-posterior areas.

Magnetic susceptibility source separation (χ-separation), an advanced quantitative susceptibility mapping (QSM) method, enables the separate estimation of paramagnetic and diamagnetic susceptibility source distributions in the brain. Similar to QSM, it requires solving the ill-conditioned problem of dipole inversion, suffering from so-called streaking artifacts. Additionally, the method utilizes reversible transverse relaxation ( ) to complement frequency shift information for estimating susceptibility source concentrations, requiring time-consuming data acquisition for (e.g., multi-echo spin-echo) in addition to multi-echo GRE data for . To address these challenges, we develop a new deep learning network, χ-sepnet, and propose two deep learning-based susceptibility source separation pipelines, χ-sepnet- for inputs with multi-echo GRE and multi-echo spin-echo (or turbo spin-echo) and χ-sepnet- for input with multi-echo GRE only. The neural network is trained using multiple head orientation data that provide streaking artifact-free labels, generating high-quality χ-separation maps. The evaluation of the pipelines encompasses both qualitative and quantitative assessments in healthy subjects, and visual inspection of lesion characteristics in multiple sclerosis patients. The susceptibility source-separated maps of the proposed pipelines delineate detailed brain structures with substantially reduced artifacts compared to those from the conventional regularization-based reconstruction methods. In quantitative analysis, χ-sepnet- achieves the best outcomes followed by χ-sepnet- , outperforming the conventional methods. When the lesions of multiple sclerosis patients are classified into subtypes, most lesions are identified as the same subtype in the maps from χ-sepnet- and χ-sepnet- (paramagnetic susceptibility: 99.6% and diamagnetic susceptibility: 98.4%; both out of 250 lesions). The χ-sepnet- pipeline, which only requires multi-echo GRE data, has demonstrated its potential to offer broad clinical and scientific applications, although further evaluations for various diseases and pathological conditions are necessary.

Spontaneous neural activity coherently relays information across the brain. Several efforts have been made to understand how spontaneous neural activity evolves at the macro-scale level as measured by resting-state functional magnetic resonance imaging (rsfMRI). Previous studies observe the global patterns and flow of information in rsfMRI using methods such as sliding window or temporal lags. However, to our knowledge, no studies have examined spatial propagation patterns evolving with time across multiple overlapping 4D networks. Here, we propose a novel approach to study how dynamic states of the brain networks spatially propagate and evaluate whether these propagating states contain information relevant to mental illness. We implement a lagged windowed correlation approach to capture voxel-wise network-specific spatial propagation patterns in dynamic states. Results show systematic spatial state changes over time, which we confirmed are replicable across multiple scan sessions using human connectome project data. We observe networks varying in propagation speed; for example, the default mode network (DMN) propagates slowly and remains positively correlated with blood oxygenation level-dependent (BOLD) signal for 6-8 s, whereas the visual network propagates much quicker. We also show that summaries of network-specific propagative patterns are linked to schizophrenia. More specifically, we find significant group differences in multiple dynamic parameters between patients with schizophrenia and controls within four large-scale networks: default mode, temporal lobe, subcortical, and visual network. Individuals with schizophrenia spend more time in certain propagating states. In summary, this study introduces a promising general approach to exploring the spatial propagation in dynamic states of brain networks and their associated complexity and reveals novel insights into the neurobiology of schizophrenia.

Cognitive impairment is considered to be one of the key features of Parkinson's disease (PD), ultimately resulting in PD-related dementia in approximately 80% of patients over the course of the disease. Several distinct cognitive syndromes of PD have been suggested, driven by different neurotransmitter deficiencies and thus requiring different treatment regimes. In this study, we aimed to identify characteristic brain covariance patterns that reveal how cholinergic denervation is related to PD and to cognitive impairment, focusing on four domains, including attention, executive functioning, memory, and visuospatial cognition. We applied scaled sub-profile model principal component analysis to reveal cholinergic-specific disease-related and cognition-related covariance patterns using [F]fluoroethoxybenzovesamicol PET imaging. Stepwise logistic regression was applied to predict disease state (PD vs. healthy control). Linear regression models were applied to predict cognitive functioning within the PD group, for each cognitive domain separately. We assessed the performance of the identified patterns with leave-one-out cross validation and performed bootstrapping to assess pattern stability. We included 34 PD patients with various levels of cognitive dysfunction and 10 healthy controls, with similar age, sex, and educational level. The disease-related cholinergic pattern was strongly discriminative (AUC 0.91), and was most prominent in posterior brain regions, with lower tracer uptake in patients compared to controls. We found largely overlapping cholinergic-specific patterns across cognitive domains, with positive correlations between tracer uptake in the opercular cortex, left dorsolateral prefrontal cortex and posterior cingulate gyrus, among other regions, and attention, executive, and visuospatial functioning. Cross validation showed significant correlations between predicted and measured cognition scores, with the exception of memory. We identified a robust structural covariance pattern for the assessment of cholinergic dysfunction related to PD, as well as overlapping cholinergic patterns related to attentional, executive- and visuospatial impairment in PD patients.

Neuroimaging research has increasingly used decoding techniques, in which multivariate statistical methods identify patterns in neural data that allow the classification of experimental conditions or participant groups. Typically, the features used for decoding are spatial in nature, including voxel patterns and electrode locations. However, the strength of many neurophysiological recording techniques such as electroencephalography or magnetoencephalography is in their rich temporal, rather than spatial, content. The present report introduces the time-GAL toolbox, which implements a decoding method based on time information in electrophysiological recordings. The toolbox first quantifies the decodable information contained in neural time series. This information is then used in a subsequent step, generalization across location (GAL), which characterizes the relationship between sensor locations based on their ability to cross-decode. Two datasets are used to demonstrate the usage of the toolbox, involving (1) event-related potentials in response to affective pictures and (2) steady-state visual evoked potentials in response to aversively conditioned grating stimuli. In both cases, experimental conditions were successfully decoded based on the temporal features contained in the neural time series. Spatial cross-decoding occurred in regions known to be involved in visual and affective processing. We conclude that the approach implemented in the time-GAL toolbox holds promise for analyzing neural time series from a wide range of paradigms and measurement domains providing an assumption-free method to quantifying differences in temporal patterns of neural information processing and whether these patterns are shared across sensor locations.

Converging lines of research indicate that inhibitory control is likely to be compromised in contexts that place competing demands on emotional, motivational, and cognitive systems, potentially leading to damaging impulsive behavior. The objective of this study was to identify the neural impact of three challenging contexts that typically compromise self-regulation and weaken impulse control. Participants included 66 healthy adults (M/SD = 29.82/10.21 years old, 63.6% female) who were free of psychiatric disorders and psychotropic medication use. Participants completed a set of novel Go/NoGo (GNG) paradigms in the scanner, which manipulated contextual factors to induce (i) aversive emotions, (ii) appetitive drive, or (iii) concurrent working memory load. Voxelwise analysis of neural activation during each of these tasks was compared to that of a neutral GNG task. Findings revealed differential inhibition-related activation in the aversive emotions and appetitive drive GNG tasks relative to the neutral task in frontal, parietal and temporal cortices, suggesting emotional and motivational contexts may suppress activation of these cortical regions during inhibitory control. In contrast, the GNG task with a concurrent working memory load showed widespread increased activation across the cortex compared to the neutral task, indicative of enhanced recruitment of executive control regions. Results suggest the neural circuitry recruited for inhibitory control varies depending on the concomitant emotional, motivational, and cognitive demands of a given context. This battery of GNG tasks can be used by researchers interested in studying unique patterns of neural activation associated with inhibitory control across three clinically relevant contexts that challenge self-regulation and confer risk for impulsive behavior.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI. In this study, we evaluate a novel technique for robust computational assessment of motion artifacts, and the quantitative comparison of regression models for artifact removal in fetal FC analysis. It exploits the association between dynamic FC and non-stationarity of fetal movement, to detect residual noise. To validate our motion artifact detection technique in detail, we used a parametric generative model for neural events and fMRI blood oxygenation level-dependent (BOLD) signal. We conducted a systematic evaluation of 11 commonly used regression models in a sample of 70 fetuses with gestational age of 19-39 weeks. Results demonstrate that the proposed method has better accuracy in identifying corrupted FC compared to methods designed for adults. The technique, suggests that censoring, global signal regression and anatomical component-based regression models are the most effective models for compensating motion. The benchmarking technique, and the generative model for realistic fetal fMRI BOLD enables investigators conducting in utero fMRI analysis to effectively quantify the impact of fetal motion and evaluate alternative regression strategies for mitigating this impact. The code is publicly available at: https://github.com/cirmuw/fetalfMRIproc.

Over a third of minor stroke patients experience post-stroke cognitive impairment (PSCI), but no validated tools exist to identify at-risk patients early. This study investigated whether disconnection features derived from infarcts and white matter hyperintensities (WMH) could serve as markers for short- and long-term cognitive decline in first-ever minor ischemic stroke patients. First-ever minor ischemic stroke patients (NIHSS ≤ 7) were prospectively followed at 72-h, 6 months, and 36 months post-stroke with cognitive tests and brain MRI. Infarct and WMH volumes were semi-automatically assessed on DWI and FLAIR sequences. Bayesian tract-based disconnection models estimated remote pathological effects of infarcts and WMH. Associations between disconnection features and cognitive outcomes were analyzed using canonical correlation analyses, adjusted for age, education, and multiple comparisons. Among 105 patients (31% female, mean age 63 ± 12 years), infarct volume averaged 10.28 ± 17.10 cm and predominantly involved the middle cerebral artery territory (83%). WMH burden was higher in frontal periventricular white matter. Infarct-based features did not significantly relate to PCSI. However, a WMH-derived disconnection factor, involving commissural and frontal tracts, and the right superior longitudinal fasciculus, was significantly associated with PSCI at 6 months (OR = 9.96, p value = 0.02) and 36 months (OR = 12.27, p value = 0.006), particularly in executive/attention, language, and visuospatial domains. This factor, unrelated to WMH volume, outperformed demographic and clinical predictors of PSCI. WMH-induced disconnection may be associated with short- and long-term PSCI in minor stroke. Routine MR-derived features could identify at-risk patients for rehabilitation trials.

Population receptive field (pRF) mapping is a quantitative functional MRI (fMRI) analysis method that links visual field positions with specific locations in the visual cortex. A common preprocessing step in pRF analyses involves projecting volumetric fMRI data onto the cortical surface, typically leading to upsampling of the data. This process may introduce biases in the resulting pRF parameters. Using publicly available analysis containers, we compared pRF maps generated from the original volumetric with those from upsampled surface data. Our results show substantial increases in pRF coverage in the central visual field of upsampled datasets. These effects were consistent across early visual cortex areas V1-3. Further analysis indicates that this bias is primarily driven by the nonlinear relationship between cortical distance and visual field eccentricity, known as cortical magnification. Our results underscore the importance of understanding and addressing biases introduced by processing steps to ensure accurate interpretation of pRF mapping data, particularly in cross-study comparisons.

Neurodegeneration is presumed to be the pathological process measure most proximal to clinical symptom onset in Alzheimer Disease (AD). Structural MRI is routinely collected in research and clinical trial settings. Several quantitative MRI-based measures of atrophy have been proposed, but their low correspondence with each other has been previously documented. The purpose of this study was to identify which commonly used structural MRI measure (hippocampal volume, cortical thickness in AD signature regions, or brain age gap [BAG]) had the best correspondence with the Clinical Dementia Rating (CDR) in an ethno-racially diverse sample. 2870 individuals recruited by the Healthy and Aging Brain Study-Health Disparities completed both structural MRI and CDR evaluation. Of these, 1887 individuals were matched on ethno-racial identity (Mexican American [MA], non-Hispanic Black [NHB], and non-Hispanic White [NHW]) and CDR (27% CDR > 0). We estimated brain age using two pipelines (DeepBrainNet, BrainAgeR) and then calculated BAG as the difference between the estimated brain age and chronological age. We also quantified their hippocampal volumes using HippoDeep and cortical thicknesses (both an AD-specific signature and average whole brain) using FreeSurfer. We used ordinal regression to evaluate associations between neuroimaging measures and CDR and to test whether these associations differed between ethno-racial groups. Higher BAG (p = 0.0002; p = 0.00117) and lower hippocampal volume (p = 0.0015) and cortical thickness (p < 0.0001) were associated with worse clinical status (higher CDR). AD signature cortical thickness had the strongest relationship with CDR (AIC = 2623, AIC = 2588, AIC = 2533, AIC = 2293, AIC = 1903). The relationship between CDR and atrophy measures differed between ethno-racial groups for both BAG estimates and hippocampal volume, but not for cortical thickness. We interpret the lack of an interaction between ethno-racial identity and AD signature cortical thickness on CDR as evidence that cortical thickness effectively captures sources of disease-related atrophy that may differ across racial and ethnic groups. Cortical thickness had the strongest association with CDR. These results suggest that cortical thickness may be a more sensitive and generalizable marker of neurodegeneration than hippocampal volume or BAG in ethno-racially diverse cohorts.

Accurate metacognitive judgments about an individual's performance in a mental task require the brain to have access to representations of the quality and difficulty of first-order cognitive processes. However, little is known about how accurate metacognitive judgments are implemented in the brain. Here, we combine brain stimulation with functional neuroimaging to determine the neural and psychological mechanisms underlying the frontopolar cortex's (FPC) role in metacognition. Specifically, we evaluate two-layer neural architectures positing that FPC enables metacognitive judgments by communicating with brain regions encoding first-order decision difficulty. In support of two-layer architectures of metacognition, we found that high-intensity transcranial alternating current stimulation (tACS; 4 mA peak-to-peak) over FPC impaired metacognitive accuracy; at the neural level, this impairment was reflected by reduced coupling between FPC and dorsolateral prefrontal cortex (DLPFC), particularly during difficult metacognitive judgments. We also evaluated conceptual accounts assuming that metacognition relies on self-directed mentalizing. However, we observed no influence of FPC tACS on mentalizing performance and only a weak overlap of the networks underlying metacognition and mentalizing. Together, our findings put the FPC at the center of a two-layer architecture that enables accurate evaluations of cognitive processes, mainly via the FPC's connectivity with regions encoding first-level task difficulty, with little contributions from mentalizing-related processes.

Pattern separation and pattern completion in the hippocampus play a critical role in episodic learning and memory. However, there is limited empirical evidence supporting the role of the hippocampal circuit in these processes during complex continuous experiences. In this study, we analyzed high-resolution fMRI data from the "Forrest Gump" open-access dataset (16 participants) using a sliding-window temporal autocorrelation approach to investigate whether the canonical hippocampal circuit (DG-CA3-CA1-SUB) shows evidence consistent with the occurrence of pattern separation or pattern completion during a naturalistic audio movie task. Our results revealed that when processing continuous naturalistic stimuli, the DG-CA3 pair exhibited evidence consistent with the occurrence of the pattern separation process, whereas both the CA3-CA1 and CA1-SUB pairs showed evidence consistent with pattern completion. Moreover, during the latter half of the audio movie, we observed evidence consistent with a reduction in pattern completion in the CA3-CA1 pair and an increase in pattern completion in the CA1-SUB pair. Overall, these findings improve our understanding of the evidence related to the occurrence of pattern separation and pattern completion processes during natural experiences.

Apathy is a common neuropsychiatric symptom following stroke, characterized by reduced goal-directed behavior. The reward decision network (RDN), which plays a crucial role in regulating goal-directed behaviors, is closely associated with apathy. However, the relationship between poststroke apathy (PSA) and RDN dysfunction remains unclear due to apathy heterogeneity, the confounding effect of depression and individual variability in lesion impacts. This study aims to dissect the heterogeneity of PSA and explore the link between lesion-induced RDN damage and PSA. We prospectively recruited 207 patients with acute ischemic infarction and 60 demographically matched healthy controls. Participants underwent neuroimaging and longitudinal neuropsychiatric assessments. To characterize PSA heterogeneity, we employed multivariate analysis and clustering algorithms based on whole-brain functional connectivity and clinical assessments to classify patients into different PSA biotypes. We embedded each patient's lesion into a structural connectome atlas to obtain white matter (WM) disconnection maps. On this basis, WM disconnection scores were calculated for each brain region to quantify lesion-induced WM damage. We employed the XGBoost model to predict PSA biotypes based on WM disconnection scores, comparing the performance of models focusing on RDN-specific versus whole-brain WM disconnection. Additionally, we explored WM damage patterns across different biotypes by comparing disconnection scores in critical brain regions. We identified four PSA biotypes with unique clinical trajectories and neurobiological underpinnings. Biotype 4 was characterized by persistent apathy with depressive symptoms. Biotype 2 showed persistent apathy. Biotype 3 was non-apathetic. Biotype 1 exhibited delayed-onset apathy. The XGBoost models, when focused on the RDN-specific WM disconnection, performed significantly better in predicting PSA biotypes compared to the whole-brain WM disconnection model (t(164.66) = 8.871, p < 0.001). Analysis of WM disconnection patterns revealed that Biotype 4 exhibited more extensive RDN damage in crucial regions, Biotype 1 had a unique pattern of damage in the anterior cingulate cortex (t(61) = 1.874, p = 0.032), and Biotype 2 had a unique pattern of damage in the orbitofrontal cortex (t(53)= 1.827, p = 0.036). This study dissected PSA heterogeneity and demonstrated that RDN damage is a critical factor in PSA variability. We found that lesion-induced WM disconnections in anterior cingulate cortex and orbitofrontal cortex can lead to delayed-onset and persistent apathy, respectively. Furthermore, our findings revealed that apathy not only has distinct pathogenic mechanisms, but also shares neurobiological substrates with depression.

Iron in the brain is essential to neurodevelopmental processes, as it supports neural functions, including processes of oxygen delivery, electron transport, and enzymatic activity. However, the development of brain iron before birth is scarcely understood. By estimating R2* (1/T2*) relaxometry from a sizable sample of fetal multiecho echo-planar imaging (EPI) scans, which is the standard sequence for functional magnetic resonance imaging (fMRI), across gestation, this study investigates age and sex-related changes in iron, across regions and tissue segments. Our findings reveal that brain R2* levels significantly increase throughout gestation spanning many different regions, except the frontal lobe. Furthermore, females exhibit a faster rate of R2* increase compared to males, in both gray matter and white matter. This sex effect is particularly notable within the left insula. This work represents the first MRI examination of iron accumulation and sex differences in developing fetal brains. This is also the first study to establish R2* estimation methodology in fetal multiecho functional MRI.

Functional brain fingerprinting has emerged as an influential tool to quantify reliability in neuroimaging studies and to identify cognitive biomarkers in both healthy and clinical populations. Recent studies have revealed that brain fingerprints reside in the timescale-specific functional connectivity of particular brain regions. However, the impact of the acquisition's temporal resolution on fingerprinting remains unclear. In this study, we examine for the first time the reliability of functional fingerprinting derived from resting-state functional MRI (rs-fMRI) with different whole-brain temporal resolutions (TR = 0.5, 0.7, 1, 2, and 3 s) in a cohort of 20 healthy volunteers. Our findings indicate that subject identifiability within a fixed TR is successful across different temporal resolutions, with the highest identifiability observed at TR 0.5 and 3 s (TR(s)/identifiability(%): 0.5/64; 0.7/47; 1/44; 2/44; 3/56). We discuss this observation in terms of protocol-specific effects of physiological noise aliasing. We further show that, irrespective of TR, associative brain areas make an higher contribution to subject identifiability (functional connections with highest mean ICC: within subcortical network [SUB; ICC = 0.0387], within default mode network [DMN; ICC = 0.0058]; between DMN and somato-motor [SM] network [ICC = 0.0013]; between ventral attention network [VA] and DMN [ICC = 0.0008]; between VA and SM [ICC = 0.0007]), whereas sensory-motor regions become more influential when integrating data from different TRs (functional connections with highest mean ICC: within fronto-parietal network [ICC = 0.382], within dorsal attention network [DA; ICC = 0.373]; within SUB [ICC = 0.367]; between visual network [VIS] and DA [ICC = 0.362]; within VIS [ICC = 0.358]). We conclude that functional connectivity fingerprinting derived from rs-fMRI holds significant potential for multicentric studies also employing protocols with different temporal resolutions. However, it remains crucial to consider fMRI signal's sampling rate differences in subject identifiability between data samples, in order to improve reliability and generalizability of both whole-brain and specific functional networks' results. These findings contribute to a better understanding of the practical application of functional connectivity fingerprinting, and its implications for future neuroimaging research.

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions. To test this hypothesis, we examined covariation between the volumes of anterior sensorimotor and lateral cognitive lobules of the cerebellum and measures of cortical thickness (CT) and surface area (SA) across the whole brain in a sample of 270 young adults drawn from the HCP dataset. We observed that patterns of cerebellar-cortical covariance differed between sensorimotor and cognitive networks. Anterior motor lobules of the cerebellum showed greater covariance with sensorimotor regions of the cortex, while lobules Crus I and Crus II showed greater covariance with frontal and temporal regions. Interestingly, cerebellar volume showed predominantly negative relationships with CT and predominantly positive relationships with SA. Individual differences in SA are thought to be largely under genetic control while CT is thought to be more malleable by experience. This suggests that cerebellar-cortical covariation for SA may be a more stable feature, whereas covariation for CT may be more affected by development. Additionally, similarity metrics revealed that the pattern of covariance showed a gradual transition between sensorimotor and cognitive lobules, consistent with evidence of functional gradients within the cerebellum. Taken together, these findings are consistent with known patterns of structural and functional connectivity between the cerebellum and cortex. They also shed new light on possibly differing relationships between cerebellar volume and cortical thickness and surface area. Finally, our findings are consistent with the interactive specialization framework which proposes that structurally and functionally connected brain regions develop in concert.

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been investigated for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 62 fetal brain scans and extracted a set of 51 distinct white matter tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20%-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75% and 80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. These results highlight the relevance of these regions to key developmental processes, specifically, the dispersion of projection fibers, the protracted growth of commissural pathways, and the emergence of association tracts that contribute to the formation of complex intersection regions. These developmental interactions lead to a high prevalence of crossing fibers and bottleneck areas, reflecting the intricate organization required for establishing structural and functional connectivity. Additionally, our results highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

A fundamental issue in neuroscience is a lack of understanding regarding the relationship between brain function and the white matter architecture that supports it. Individuals with chronic neuropathic pain (NP) exhibit functional abnormalities throughout brain networks collectively termed the "dynamic pain connectome" (DPC), including the default mode network (DMN), salience network, and ascending nociceptive and descending pain modulation systems. These functional abnormalities are often observed in a sex-dependent fashion. However, the enigmatic white matter structural features underpinning these functional networks and the relationship between structure and function/dysfunction in NP remain poorly understood. Here we used fixel-based analysis of diffusion weighted imaging data in 80 individuals (40 with NP [21 female, 19 male] and 40 sex- and age-matched healthy controls [HCs]) to evaluate white matter microstructure (fiber density [FD]), macrostructure (fiber bundle cross section) and combined microstructure and macrostructure (fiber density and cross section) within anatomical connections that support the DPC. We additionally examined whether there are sex-specific abnormalities in NP white matter structure. We performed fixel-wise and connection-specific mean analyses and found three main ways in which individuals with NP differed from HCs: (1) people with NP exhibited abnormally low FD and FDC within the corona radiata consistent with the ascending nociceptive pathway between the sensory thalamus and primary somatosensory cortex (S1). Furthermore, the entire sensory thalamus-S1 pathway exhibited abnormally low FD and FDC in people with NP, and this effect was driven by the females with NP; (2) females, but not males, with NP had abnormally low FD within the cingulum consistent with the right medial prefrontal cortex-posterior cingulate cortex DMN pathway; and (3) individuals with NP had higher connection-specific mean FDC than HCs in the anterior insula-temporoparietal junction and sensory thalamus-posterior insula pathways. However, sex-specific analyses did not corroborate these connection-specific findings in either females or males with NP. Our findings suggest that females with NP exhibit microstructural and macrostructural white matter abnormalities within the DPC networks including the ascending nociceptive system and DMN. We propose that aberrant white matter structure contributes to or is driven by functional abnormalities associated with NP. Our sex-specific findings highlight the utility and importance of using sex-disaggregated analyses to identify white matter abnormalities in clinical conditions such as chronic pain.

Premature infants, born before 37 weeks of gestation can have alterations in neurodevelopment and cognition, even when no anatomical lesions are evident. Resting-state functional neuroimaging of naturally sleeping babies has shown altered connectivity patterns, but there is limited evidence on the developmental trajectories of functional organization in preterm neonates. By using a large dataset from the developing Human Connectome Project, we explored the differences in graph theory properties between at-term (n = 332) and preterm (n = 115) neonates at term-equivalent age, considering the age subgroups proposed by the World Health Organization for premature birth. Leveraging the longitudinal follow-up for some preterm participants, we characterized the developmental trajectories for preterm and at-term neonates, for this purpose linear, quadratic, and log-linear mixed models were constructed with gestational age at scan as an independent fixed-effect variable and random effects were added for the intercept and subject ID. Significance was defined at p < 0.05, and the model with the lowest Akaike Information Criterion (AIC) was selected as the best model. We found significant differences between groups in connectivity strength, clustering coefficient, characteristic path length and global efficiency. Specifically, at term-equivalent ages, higher connectivity, clustering coefficient and efficiency are identified for neonates born at later postmenstrual ages. Similarly, the characteristic path length showed the inverse pattern. These results were consistent for a variety of connectivity thresholds at both the global (whole brain) and local level (brain regions). The brain regions with the greatest differences between groups include primary sensory and motor regions and the precuneus which may relate to the risk factors for sensorimotor and behavioral deficits associated with premature birth. Our results also show non-linear developmental trajectories for premature neonates, but decreased integration and segregation even at term-equivalent age. Overall, our results confirm altered functional connectivity, integration and segregation properties of the premature brain despite showing rapid maturation after birth.