Unraveling how the adolescent brain deals with criticism using dynamic causal modeling.

Sensitivity to criticism, which can be defined as a negative evaluation that a person receives from someone else, is considered a risk factor for the development of psychiatric disorders in adolescents. They may be more vulnerable to social evaluation than adults and exhibit more inadequate emotion regulation strategies such as rumination. The neural network involved in dealing with criticism in adolescents may serve as a biomarker for vulnerability to depression. However, the directions of the functional interactions between the brain regions within this neural network in adolescents are still unclear. In this study, 64 healthy adolescents (aged 14 to 17 years) were asked to listen to a series of self-referential auditory segments, which included negative (critical), positive (praising), and neutral conditions, during fMRI scanning. Dynamic Causal Modeling (DCM) with Parametric Empirical Bayesian (PEB) analysis was performed to map the interactions within the neural network that was engaged during the processing of these segments. Three regions were identified to form the interaction network: the left pregenual anterior cingulate cortex (pgACC), the left dorsolateral prefrontal cortex (DLPFC), and the right precuneus (preCUN). We quantified the modulatory effects of exposure to criticism and praise on the effective connectivity between these brain regions. Being criticized was found to significantly inhibit the effective connectivity from the preCUN to the DLPFC. Adolescents who scored high on the Perceived Criticism Measure (PCM) showed less inhibition of the preCUN-to-DLPFC connectivity when being criticized, which may indicate that they required more engagement of the Central Executive Network (which includes the DLPFC) to sufficiently disengage from negative self-referential processing. Furthermore, the inhibitory connectivity from the DLPFC to the pgACC was strengthened by exposure to praise as well as criticism, suggesting a recruitment of cognitive control over emotional responses when dealing with positive and negative evaluative feedback. Our novel findings contribute to a more profound understanding of how criticism affects the adolescent brain and can help to identify potential biomarkers for vulnerability to develop mood disorders before or during adulthood.

Solid-State Nanopore Distinguishes Ferritin and Apo-Ferritin with Identical Exteriors through Amplified Flexibility at Single-Molecule Level.

Protein identification and discrimination at the single-molecule level are big challenges. Solid-state nanopores as a sensitive biosensor have been used for protein analysis, although it is difficult to discriminate proteins with similar structures in the traditional discrimination method based on the current blockage fraction. Here, we select ferritin and apo-ferritin as the model proteins that exhibit identical exterior and different interior structures and verify the practicability of their discrimination with flexibility features by the strategy of gradually decreasing the nanopore size. We show that the larger nanopore (relative to the protein size) has no obvious effect on discriminating two proteins. Then, the comparable-sized nanopore plays a key role in discriminating two proteins based on the dwell time and fraction distribution, and the conformational changes of both proteins are also studied with this nanopore. Finally, in the smaller nanopore, the protein molecules are trapped rather than translocated, where two proteins are obviously discriminated through the current fluctuation caused by the vibration of proteins. This strategy has potential in the discrimination of other important similar proteins.

The moderating effect of resting heart rate variability on the relationship between internet addiction tendency and brain morphology.

Previous neuroimaging studies have investigated brain morphology associated with internet addiction tendency (IAT) in healthy subjects. However, whether resting vagally-mediated heart rate variability (HRV) exerting influences on the association of IAT and brain morphology remains unclear. This study used voxel-based morphometry (VBM) and multiple regression analyses to assess the interaction effect of IAT and resting vagally-mediated HRV on regional grey matter volumes in 82 healthy subjects. To further illustrate the observed interaction effect, the moderated hierarchical regression analysis was performed. The results showed that resting vagally-mediated HRV moderated the relationship between IAT scores and grey matter volume (GMV) in the precuneus and cerebellum. Specifically, individuals with higher resting vagally-mediated HRV showed a significant positive relationship between IAT scores and GMV in the precuneus, whereas individuals with lower resting vagally-mediated HRV showed a significant negative relationship between IAT scores and GMV in the precuneus. In addition, IAT scores were negatively correlated with GMV in the cerebellum among individuals with lower resting vagally-mediated HRV, but not among individuals with higher resting vagally-mediated HRV. These findings have demonstrated a moderating role of resting vagally-mediated HRV on the association of IAT and brain morphology.

Metabolic Covariance Connectivity of Posterior Cingulate Cortex Associated with Depression Symptomatology Level in Healthy Young Adults.

Early detection in the development of a Major Depressive Disorder (MDD) could guide earlier clinical interventions. Although MDD can begin at a younger age, most people have their first episode in young adulthood. The underlying pathophysiological mechanisms relating to such an increased risk are not clear. The posterior cingulate cortex (PCC), exhibiting high levels of brain connectivity and metabolic activity, plays a pivotal role in the pathological mechanism underlying MDD. In the current study, we used the (F-18) fluorodeoxyglucose (FDG) positron emission tomography (PET) to measure metabolic covariance connectivity of the PCC and investigated its association with depression symptomatology evaluated by the Centre for Epidemiological Studies Depression Inventory-Revised (CESD-R) among 27 healthy individuals aged between 18 and 23 years. A significant negative correlation has been observed between CESD-R scale scores and the PCC metabolic connectivity with the anterior cingulate, medial prefrontal cortex, inferior and middle frontal gyrus, as well as the insula. Overall, our findings suggest that the neural correlates of depressive symptomatology in healthy young adults without a formal diagnosis involve the metabolic connectivity of the PCC. Our findings may have potential implications for early identification and intervention in people at risk of developing depression.

The impact of sensation seeking personality trait on acute alcohol-induced disinhibition.

BACKGROUND: Acute alcohol-related behavioral disinhibition has been well studied. But whether individual differences in the personality trait sensation seeking affect alcohol-induced behavioral disinhibition remains uncertain. METHODS: The present study used functional near-infrared spectroscopy (fNIRS) technique and a response inhibition task (i.e., Go/No-Go) to determine the impact of the sensation seeking trait on the relationship between acute alcohol administration and inhibitory control capacity, and further investigate the neural mechanisms underlying this behavioral effect. Twenty-five high-sensation seekers and twenty-six low-sensation seekers were enrolled in this study. These participants attended two sessions: once for alcohol intake (0.5g/kg) and once for placebo intake (0g/kg). RESULTS: Our results showed that high-sensation seekers relative to low-sensation seekers showed a significant decrease in inhibition accuracy under alcohol versus the placebo condition. Moreover, reduced prefrontal activity following acute alcohol consumption was more pronounced in high-sensation seekers compared with low-sensation seekers. CONCLUSIONS: These findings showed that alcohol-induced behavioral disinhibition was affected by the personality trait sensation seeking and that recruitment of the prefrontal cortex contributed to the observed behavioral effect.

Normative modeling analysis reveals corpus callosum volume changes in early and mid-to-late first episode major depression.

BACKGROUND: It has been widely accepted that major depressive disorder (MDD) impacts brain structures including the Corpus Callosum (CC). However, this assumption is based on scarce literature data involving small sample sizes. Furthermore, it is still unclear whether such CC volume changes may already be present at a first depressive episode. METHODS: To further investigate this question, we compared 369 first-episode MDD patients (mean age = 35 years (sd = 12), 249 females; 283 early onset, 86 mid-to-late onset) from the open-source REST meta-MDD database closely matched for age and gender to 490 never-depressed individuals (mean age = 37 years (sd = 14); 309 females) using Z-scores obtained from normative neuroanatomical modeling to assess individual variability in CC (sub)volumes. RESULTS: Relative to the norms established by the healthy controls, first-episode MDD patients displayed CC volume (z-score) reductions in the entire CC (including the body), as did mid-to-late-onset first-episode MDD patients (age ≥ 45 y). In early-onset first-episode MDD patients (age ≤ 44 y), depression severity symptoms were related to volume increases in the entire CC, as well as the body and splenium. LIMITATIONS: No data on depressive episode duration. Relatively small sample size for mid-to-late first-episode MDD patients. CONCLUSIONS: Our data revealed CC (sub)volume differences in early versus mid-to-late onset first episode MDD. Especially at early onset, depression severity may result in neural white matter activity as potential reaction to stress influences. Our results underline the importance of prompt clinical interventions at early onset MDD.

Structural connectome alterations in anxious dogs: a DTI-based study.

Anxiety and fear are dysfunctional behaviors commonly observed in domesticated dogs. Although dogs and humans share psychopathological similarities, little is known about how dysfunctional fear behaviors are represented in brain networks in dogs diagnosed with anxiety disorders. A combination of diffusion tensor imaging (DTI) and graph theory was used to investigate the underlying structural connections of dysfunctional anxiety in anxious dogs and compared with healthy dogs with normal behavior. The degree of anxiety was assessed using the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), a widely used, validated questionnaire for abnormal behaviors in dogs. Anxious dogs showed significantly decreased clustering coefficient ([Formula: see text]), decreased global efficiency ([Formula: see text]), and increased small-worldness (σ) when compared with healthy dogs. The nodal parameters that differed between the anxious dogs and healthy dogs were mainly located in the posterior part of the brain, including the occipital lobe, posterior cingulate gyrus, hippocampus, mesencephalon, and cerebellum. Furthermore, the nodal degree ([Formula: see text]) of the left cerebellum was significantly negatively correlated with "excitability" in the C-BARQ of anxious dogs. These findings could contribute to the understanding of a disrupted brain structural connectome underlying the pathological mechanisms of anxiety-related disorders in dogs.

Precision targeting in prediction for rTMS clinical outcome in depression: what about sgACC lateralization, metabolic connectivity, and the potential role of the cerebellum?

Predicting clinical response to repetitive transcranial magnetic stimulation (rTMS) in medication-resistant depression (MRD) has gained great importance in recent years. Mainly, the right subgenual anterior cingulate cortex (sgACC) functional connectivity has been put forward as biomarker in relation to rTMS clinical outcome. Even though the left and right sgACC may have different neurobiological functions, little is known about the possible lateralized predictive role of the sgACC in rTMS clinical outcome. In 43 right-handed antidepressant-free MRD patients, we applied a searchlight-based interregional covariance connectivity approach using the baseline 18FDG-PET scan-collected from two previous high-frequency (HF)-rTMS treatment studies delivering stimulation to the left dorsolateral prefrontal cortex (DLPFC)-and investigated whether unilateral or bilateral sgACC glucose metabolism at baseline would result in different predictive metabolic connectivity patterns. Regardless of sgACC lateralization, the weaker the sgACC seed-based baseline metabolic functional connections with the (left anterior) cerebellar areas, the significantly better the clinical outcome. However, the seed diameter seems to be crucial. Similar significant findings on sgACC metabolic connectivity with the left anterior cerebellum, also unrelated to sgACC lateralization, in relation to clinical outcome were observed when using the HCPex atlas. Although we could not substantiate that specifically right sgACC metabolic connectivity would predict HF-rTMS clinical outcome, our findings suggest considering the entire sgACC in functional connectivity predictions. Given that the interregional covariance connectivity results were significant only when using the Beck Depression Inventory (BDI-II) and not with the Hamilton Depression Rating Scale (HDRS), our sgACC metabolic connectivity observations also suggest the possible involvement of the (left) anterior cerebellum involved in higher-order cognitive processing as part of this predictive value.

Spatiotemporal topological correspondence between blood oxygenation and glucose metabolism revealed by simultaneous fPET-fMRI in brain's white matter.

White matter (WM) makes up half of the human brain. Compelling functional MRI evidence indicates that white matter exhibits neural activation and synchronization via a hemodynamic window. However, the neurometabolic underpinnings of white matter temporal synchronization and spatial topology remain unknown. By leveraging concurrent [18F]FDG-fPET and blood-oxygenation-level-dependent-fMRI, we demonstrated the temporal and spatial correspondences between blood oxygenation and glucose metabolism in the human brain white matter. In the temporal scale, we found that blood-oxygenation-level-dependent signals shared mutual information with FDG signals in the default-mode, visual, and sensorimotor-auditory networks. For spatial distribution, the blood-oxygenation-level-dependent functional networks in white matter were accompanied by substantial correspondence of FDG functional connectivity at different topological scales, including degree centrality and global gradients. Furthermore, the content of blood-oxygenation-level-dependent fluctuations in the white matter default-mode network was aligned and liberal with the FDG graph, suggesting the freedom of default-mode network neuro-dynamics, but the constraint by metabolic dynamics. Moreover, the dissociation of the functional gradient between blood-oxygenation-level-dependent and FDG connectivity specific to the white matter default-mode network revealed functional heterogeneities. Together, the results showed that brain energy metabolism was closely coupled with blood oxygenation in white matter. Comprehensive and complementary information from fMRI and fPET might therefore help decode brain white matter functions.

Combining transcranial direct current stimulation with group cognitive behavioral therapy developed to treat rumination: a clinical pilot study.

Background: As part of repetitive negative thinking (RNT), rumination is a maladaptive cognitive response style to stress or negative mood which can increase the risk of depression and may prohibit complete recovery. Cognitive behavioral therapy (CBT) and transcranial direct current stimulation (tDCS) both proved to be effective in decreasing rumination. However, the combined effects of tDCS and CBT interventions on rumination have not yet been explored. The first aim of this pilot study is to investigate whether the combination of tDCS and CBT has an accumulating positive effect on modulating state rumination. The second aim is to assess the feasibility and safety profile of the proposed combined approach. Method: Seventeen adults aged 32-60 years, suffering from RNT, were referred by their primary care professional to participate in an 8-week group intervention for RNT ("Drop It") comprising 8 sessions of CBT. Before each CBT session, patients underwent one double-blinded prefrontal active (2 mA for 20 min) or sham tDCS (anode over F3, cathode over the right supraorbital region) combined with an internal cognitive attention task focused on individual RNT, i.e., online tDCS priming. During each session, the Brief State Rumination Inventory was used to assess state rumination. Results: A mixed effects model analysis revealed no significant differences between the stimulation conditions, weekly sessions, or their interaction in terms of state rumination scores. Conclusion: Overall, the combination of online tDCS priming followed by group CBT was found to be safe and feasible. On the other hand, no significant additional effects of this combined approach on state rumination were established. Although our pilot study may have been too small to find significant clinical effects, future larger RCT studies on combined tDCS-CBT treatment protocols may reevaluate the selection of internal cognitive attention tasks and more objective neurophysiological measurements, consider the optimal timing of the combination (concurrently or sequentially), or may add additional tDCS sessions when following CBT.

Lateralized subgenual ACC metabolic connectivity patterns in refractory melancholic depression: does it matter?

Although treatment resistance to antidepressant pharmacotherapy is quite common, the phenomenon of refractory major depressive disorder (rMDD) is not well understood. Nevertheless, the metabolic activity of the subgenual anterior cingulate cortex (sgACC) has been put forward as a possible metabolic biomarker of clinical prediction and response, albeit sgACC lateralization differences in functional connectivity have not yet been extensively examined. Also not in the refractory depressed state. To examine sgACC lateralization differences in metabolic connectivity, we recruited 43 right-handed antidepressant-free unipolar melancholic rMDD patients and 32 right-handed healthy controls to participate in this 18FDG PET study and developed a searchlight-based interregional covariance connectivity approach. Compared to non-depressed individuals, sgACC covariance analysis showed stronger metabolic connections with frontolimbic brain regions known to be affected in the depressed state. Furthermore, whereas the left sgACC showed stronger metabolic connections with ventromedial prefrontal cortical regions, implicated in anhedonia, suicidal ideation, and self-referential processes, the right sgACC showed significantly stronger metabolic connections with posterior hippocampal and cerebellar regions, respectively specialized in memory and social processing. Overall, our results substantiate earlier research that the sgACC is a metabolic key player when clinically depressed and that distinct lateralized sgACC metabolic connectivity patterns are present.

Neural mechanisms underlying empathy during alcohol abstinence: evidence from connectome-based predictive modeling.

Empathy impairments have been linked to alcohol dependence even during abstinent periods. Nonetheless, the neural underpinnings of abstinence-induced empathy deficits remain unclear. In this study, we employed connectome-based predictive modeling (CPM) by using whole brain resting-state functional connectivity (rs-FC) to predict empathy capability of abstinent alcoholics (n = 47) versus healthy controls (n = 59). In addition, the generalizability of the predictive model (i.e., one group treated as a training dataset and another one treated as a test dataset) was performed to determine whether healthy controls and abstinent alcoholics share common neural fingerprints of empathy. Our results showed that abstinent alcoholics relative to healthy controls had decreased empathy capacity. Although no predictive models were observed in the abstinence group, we found that individual empathy scores in the healthy group can be reliably predicted by functional connectivity from the default mode network (DMN) to the sensorimotor network (SMN), occipital network, and cingulo-opercular network (CON). Moreover, the identified connectivity fingerprints of healthy controls could be generalized to predict empathy in the abstinence group. These findings indicate that neural circuits accounting for empathy may be disrupted by alcohol use and the impaired degree varies greatly among abstinent individuals. The large inter-individual variation may impede identification of the predictive model of empathy in alcohol abstainers.

The Cortico-basal-cerebellar Neurocircuit is Linked to Personality Trait of Novelty Seeking.

Previous neuroimaging studies have highlighted the role of the prefrontal-subcortical circuits in personality trait of novelty seeking (NS), thought to be mediated by the dopaminergic system. However, it remains largely unknown whether cortico-basal-cerebellar connections, heavily influenced by dopamine, are implicated in this temperament dimension as well. The present study aimed to further investigate the relationship between the NS trait and the cortico-basal-cerebellar pathways by using structural covariance network analysis. Ninety-five healthy female volunteers were included in this work, and NS was assessed with the Temperament and Character Inventory (TCI). Our results showed that NS scores were associated with structural connections between the cerebellum and the cerebral cortex, thalamus, and basal ganglia, substantiating the implication of the cortico-basal-cerebellar circuits in the NS construct. In addition, structural connections between visual and sensorimotor regions were also associated with NS scores, indicating that sensory and motor information processing may contribute to NS-related behaviors. Overall, the current findings may deepen our understanding of brain structural circuits related to this temperament dimension.

Functional connectivity-based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients.

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder accompanied by cognitive changes. To date, no effective therapy is available for ALS patients, partly due to disease heterogeneity and an imperfect understanding of the underlying pathophysiological processes. Reliable models that can predict cognitive and motor deficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional connectivity-based predictors of cognitive and motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition were separately measured with the revised ALS functional rating scale (ALSFRS-R) and the Montreal Cognitive Assessment (MoCA). Our results showed that functional connectivity within the default mode network (DMN) as well as between the DMN and the sensorimotor network (SMN), fronto-parietal network (FPN), and salience network (SN) were predictive for MoCA scores. Additionally, the observed connectivity patterns were also predictive for the individual ALSFRS-R scores. Our findings demonstrate that cognitive and motor impairments may share common connectivity fingerprints in ALS patients. Furthermore, the identified brain connectivity signatures may serve as novel targets for effective disease-modifying therapies.

Accelerated iTBS changes perfusion patterns in medication resistant depression.

Accelerated intermittent Theta Burst Stimulation (aiTBS) is a new non-invasive brain stimulation protocol developed to rapidly treat medication resistant depression (MRD). However, to examine potential neurobiological changes only few sham-controlled studies combining pre/post treatment measures and brain imaging data are available. Consequently, with this Arterial Spin Labeling (ASL) brain imaging study, we investigated in 45 antidepressant-free MRD patients whether clinical improvement following aiTBS treatment applied to the left dorsolateral prefrontal cortex (Trial registration: http://clinicaltrials.gov/show/NCT01832805) would be associated with specific changes in brain perfusion patterns. We primarily expected frontolimbic perfusion changes following active and not sham aiTBS. Our ASL brain imaging findings showed that active aiTBS resulted in prompt perfusion increases in functionally connected brain regions such as the ventromedial prefrontal cortex and the right inferior parietal lobule. We also observed decreased perfusion in the left parahippocampal gyrus and the right posterior cerebellar lobe after active aiTBS. On the other hand, sham aiTBS resulted in right angular perfusion decreases, an area known to be involved in placebo responses. Overall, our perfusion findings indicate that active aiTBS treatment promptly affects brain regions functionally and structurally connected to the stimulated area and known to be part of deregulated brain circuits when clinically depressed. Placebo responses may be part of the clinical effects of accelerated ITS protocols. Our current results further shed light on how accelerated rTMS treatment protocols may promptly improve depressive symptoms in MRD.

Brainstem glucose metabolism predicts reward dependence scores in treatment-resistant major depression.

BACKGROUND: It has been suggested that individual differences in temperament could be involved in the (non-)response to antidepressant (AD) treatment. However, how neurobiological processes such as brain glucose metabolism may relate to personality features in the treatment-resistant depressed (TRD) state remains largely unclear. METHODS: To examine how brainstem metabolism in the TRD state may predict Cloninger's temperament dimensions Harm Avoidance (HA), Novelty Seeking (NS), and Reward Dependence (RD), we collected 18fluorodeoxyglucose positron emission tomography (18FDG PET) scans in 40 AD-free TRD patients. All participants were assessed with the Temperament and Character Inventory (TCI). We applied a multiple kernel learning (MKL) regression to predict the HA, NS, and RD from brainstem metabolic activity, the origin of respectively serotonergic, dopaminergic, and noradrenergic neurotransmitter (NT) systems. RESULTS: The MKL model was able to significantly predict RD but not HA and NS from the brainstem metabolic activity. The MKL pattern regression model identified increased metabolic activity in the pontine nuclei and locus coeruleus, the medial reticular formation, the dorsal/median raphe, and the ventral tegmental area that contributed to the predictions of RD. CONCLUSIONS: The MKL algorithm identified a likely metabolic marker in the brainstem for RD in major depression. Although 18FDG PET does not investigate specific NT systems, the predictive value of brainstem glucose metabolism on RD scores however indicates that this temperament dimension in the TRD state could be mediated by different monoaminergic systems, all involved in higher order reward-related behavior.

Individual interregional perfusion between the left dorsolateral prefrontal cortex stimulation targets and the subgenual anterior cortex predicts response and remission to aiTBS treatment in medication-resistant depression: The influence of behavioral inhibition.

BACKGROUND: Accelerated intermittent Theta Burst Stimulation (aiTBS) has been put forward as an effective treatment to alleviate depressive symptoms. Baseline functional connectivity (FC) patterns between the left dorsolateral prefrontal cortex (DLPFC) and the subgenual anterior cortex (sgACC) have gained a lot of attention as a potential biomarker for response. However, arterial spin labeling (ASL) - measuring regional cerebral blood flow - may allow a more straightforward physiological interpretation of such interregional functional connections. OBJECTIVES: We investigated whether baseline covariance perfusion connectivity between the individually stimulated left DLPFC targets and sgACC could predict meaningful clinical outcome. Considering that individual characteristics may influence efficacy prediction, all patients were also assessed with the Behavioral Inhibition System/Behavioral Activation System (BIS/BAS) scale. METHODS: After baseline ASL scanning, forty-one medication-resistant depressed patients received twenty sessions of neuronavigated left DLPFC aiTBS in an accelerated sham-controlled crossover fashion, where all stimulation sessions were spread over four days (Trial registration: http://clinicaltrials.gov/show/NCT01832805). RESULTS: Stronger individual baseline interregional covariance perfusion connectivity patterns predicted response and/or remission. Furthermore, responders and remitters with higher BIS scores displayed stronger baseline interregional perfusion connections. CONCLUSIONS: Targeting the left DLPFC with aiTBS based on personal structural imaging data only may not be the most optimal method to enhance meaningful antidepressant responses. Individual baseline interregional perfusion connectivity could be an important added brain imaging method for individual optimization of more valid stimulation targets within the left DLPFC. Additional therapies dealing with behavioral inhibition may be warranted.

Transcriptomic and macroscopic architectures of intersubject functional variability in human brain white-matter.

Intersubject variability is a fundamental characteristic of brain organizations, and not just "noise". Although intrinsic functional connectivity (FC) is unique to each individual and varies across brain gray-matter, the underlying mechanisms of intersubject functional variability in white-matter (WM) remain unknown. This study identified WMFC variabilities and determined the genetic basis and macroscale imaging in 45 healthy subjects. The functional localization pattern of intersubject variability across WM is heterogeneous, with most variability observed in the heteromodal cortex. The variabilities of heteromodal regions in expression profiles of genes are related to neuronal cells, involved in synapse-related and glutamic pathways, and associated with psychiatric disorders. In contrast, genes overexpressed in unimodal regions are mostly expressed in glial cells and were related to neurological diseases. Macroscopic variability recapitulates the functional and structural specializations and behavioral phenotypes. Together, our results provide clues to intersubject variabilities of the WMFC with convergent transcriptomic and cellular signatures, which relate to macroscale brain specialization.

rsHRF: A toolbox for resting-state HRF estimation and deconvolution.

The hemodynamic response function (HRF) greatly influences the intra- and inter-subject variability of brain activation and connectivity, and might confound the estimation of temporal precedence in connectivity analyses, making its estimation necessary for a correct interpretation of neuroimaging studies. Additionally, the HRF shape itself is a useful local measure. However, most algorithms for HRF estimation are specific for task-related fMRI data, and only a few can be directly applied to resting-state protocols. Here we introduce rsHRF, a Matlab and Python toolbox that implements HRF estimation and deconvolution from the resting-state BOLD signal. We first provide an overview of the main algorithm, practical implementations, and then demonstrate the feasibility and usefulness of rsHRF by validation experiments with a publicly available resting-state fMRI dataset. We also provide tools for statistical analyses and visualization. We believe that this toolbox may significantly contribute to a better analysis and understanding of the components and variability of BOLD signals.