Multi-scale modelling of the epileptic brain: advantages of computational therapy exploration.

Objective: Epilepsy is a complex disease spanning across multiple scales, from ion channels in neurons to neuronal circuits across the entire brain. Over the past decades, computational models have been used to describe the pathophysiological activity of the epileptic brain from different aspects. Traditionally, each computational model can aid in optimizing therapeutic interventions, therefore, providing a particular view to design strategies for treating epilepsy. As a result, most studies are concerned with generating specific models of the epileptic brain that can help us understand the certain machinery of the pathological state. Those specific models vary in complexity and biological accuracy, with system-level models often lacking biological details.Approach: Here, we review various types of computational model of epilepsy and discuss their potential for different therapeutic approaches and scenarios, including drug discovery, surgical strategies, brain stimulation, and seizure prediction. We propose that we need to consider an integrated approach with a unified modelling framework across multiple scales to understand the epileptic brain. Our proposal is based on the recent increase in computational power, which has opened up the possibility of unifying those specific epileptic models into simulations with an unprecedented level of detail.Main results: A multi-scale epilepsy model can bridge the gap between biologically detailed models, used to address molecular and cellular questions, and brain-wide models based on abstract models which can account for complex neurological and behavioural observations.Significance: With these efforts, we move toward the next generation of epileptic brain models capable of connecting cellular features, such as ion channel properties, with standard clinical measures such as seizure severity.

A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson's disease.

Objective.Dopaminergic treatment is effective for Parkinson's disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.Approach.This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.Results.The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions.Significance.The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

From compulsivity to compulsion: the neural basis of compulsive disorders.

Compulsive behaviour, an apparently irrational perseveration in often maladaptive acts, is a potential transdiagnostic symptom of several neuropsychiatric disorders, including obsessive-compulsive disorder and addiction, and may reflect the severe manifestation of a dimensional trait termed compulsivity. In this Review, we examine the psychological basis of compulsions and compulsivity and their underlying neural circuitry using evidence from human neuroimaging and animal models. Several main elements of this circuitry are identified, focused on fronto-striatal systems implicated in goal-directed behaviour and habits. These systems include the orbitofrontal, prefrontal, anterior cingulate and insular cortices and their connections with the basal ganglia as well as sensoriomotor and parietal cortices and cerebellum. We also consider the implications for future classification of impulsive-compulsive disorders and their treatment.

Brain activation and connection across resting and motor-task states in patients with generalized tonic-clonic seizures.

AIMS: Motor abnormalities have been identified as one common symptom in patients with generalized tonic-clonic seizures (GTCS) inspiring us to explore the disease in a motor execution condition, which might provide novel insight into the pathomechanism. METHODS: Resting-state and motor-task fMRI data were collected from 50 patients with GTCS, including 18 patients newly diagnosed without antiepileptic drugs (ND_GTCS) and 32 patients receiving antiepileptic drugs (AEDs_GTCS). Motor activation and its association with head motion and cerebral gradients were assessed. Whole-brain network connectivity across resting and motor states was further calculated and compared between groups. RESULTS: All patients showed over-activation in the postcentral gyrus and the ND_GTCS showed decreased activation in putamen. Specifically, activation maps of ND_GTCS showed an abnormal correlation with head motion and cerebral gradient. Moreover, we detected altered functional network connectivity in patients within states and across resting and motor states by using repeated-measures analysis of variance. Patients did not show abnormal connectivity in the resting state, while distributed abnormal connectivity in the motor-task state. Decreased across-state network connectivity was also found in all patients. CONCLUSION: Convergent findings suggested the over-response of activation and connection of the brain to motor execution in GTCS, providing new clues to uncover motor susceptibility underlying the disease.

Clinical and molecular outcomes from the 5-Year natural history study of SSADH Deficiency, a model metabolic neurodevelopmental disorder.

BACKGROUND: Succinic semialdehyde dehydrogenase deficiency (SSADHD) represents a model neurometabolic disease at the fulcrum of translational research within the Boston Children's Hospital Intellectual and Developmental Disabilities Research Centers (IDDRC), including the NIH-sponsored natural history study of clinical, neurophysiological, neuroimaging, and molecular markers, patient-derived induced pluripotent stem cells (iPSC) characterization, and development of a murine model for tightly regulated, cell-specific gene therapy. METHODS: SSADHD subjects underwent clinical evaluations, neuropsychological assessments, biochemical quantification of γ-aminobutyrate (GABA) and related metabolites, electroencephalography (standard and high density), magnetoencephalography, transcranial magnetic stimulation, magnetic resonance imaging and spectroscopy, and genetic tests. This was parallel to laboratory molecular investigations of in vitro GABAergic neurons derived from induced human pluripotent stem cells (hiPSCs) of SSADHD subjects and biochemical analyses performed on a versatile murine model that uses an inducible and reversible rescue strategy allowing on-demand and cell-specific gene therapy. RESULTS: The 62 SSADHD subjects [53% females, median (IQR) age of 9.6 (5.4-14.5) years] included in the study had a reported symptom onset at ∼ 6 months and were diagnosed at a median age of 4 years. Language developmental delays were more prominent than motor. Autism, epilepsy, movement disorders, sleep disturbances, and various psychiatric behaviors constituted the core of the disorder's clinical phenotype. Lower clinical severity scores, indicating worst severity, coincided with older age (R= -0.302, p = 0.03), as well as age-adjusted lower values of plasma γ-aminobutyrate (GABA) (R = 0.337, p = 0.02) and γ-hydroxybutyrate (GHB) (R = 0.360, p = 0.05). While epilepsy and psychiatric behaviors increase in severity with age, communication abilities and motor function tend to improve. iPSCs, which were differentiated into GABAergic neurons, represent the first in vitro neuronal model of SSADHD and express the neuronal marker microtubule-associated protein 2 (MAP2), as well as GABA. GABA-metabolism in induced GABAergic neurons could be reversed using CRISPR correction of the pathogenic variants or mRNA transfection and SSADHD iPSCs were associated with excessive glutamatergic activity and related synaptic excitation. CONCLUSIONS: Findings from the SSADHD Natural History Study converge with iPSC and animal model work focused on a common disorder within our IDDRC, deepening our knowledge of the pathophysiology and longitudinal clinical course of a complex neurodevelopmental disorder. This further enables the identification of biomarkers and changes throughout development that will be essential for upcoming targeted trials of enzyme replacement and gene therapy.

The 'PSILAUT' protocol: an experimental medicine study of autistic differences in the function of brain serotonin targets of psilocybin.

BACKGROUND: The underlying neurobiology of the complex autism phenotype remains obscure, although accumulating evidence implicates the serotonin system and especially the 5HT2A receptor. However, previous research has largely relied upon association or correlation studies to link differences in serotonin targets to autism. To directly establish that serotonergic signalling is involved in a candidate brain function our approach is to change it and observe a shift in that function. We will use psilocybin as a pharmacological probe of the serotonin system in vivo. We will directly test the hypothesis that serotonergic targets of psilocybin - principally, but not exclusively, 5HT2A receptor pathways-function differently in autistic and non-autistic adults. METHODS: The 'PSILAUT' "shiftability" study is a case-control study autistic and non-autistic adults. How neural responses 'shift' in response to low doses (2 mg and 5 mg) of psilocybin compared to placebo will be examined using multimodal techniques including functional MRI and EEG. Each participant will attend on up to three separate visits with drug or placebo administration in a double-blind and randomized order. RESULTS: This study will provide the first direct evidence that the serotonin targets of psilocybin function differently in the autistic and non-autistic brain. We will also examine individual differences in serotonin system function. CONCLUSIONS: This work will inform our understanding of the neurobiology of autism as well as decisions about future clinical trials of psilocybin and/or related compounds including stratification approaches. TRIAL REGISTRATION: NCT05651126.

Differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognitive function between untreated major depressive disorder and schizophrenia with depressive mood patients.

BACKGROUND: Distinguishing untreated major depressive disorder without medication (MDD) from schizophrenia with depressed mood (SZDM) poses a clinical challenge. This study aims to investigate differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognition in untreated MDD and SZDM patients. METHODS: The study included 42 untreated MDD cases, 30 SZDM patients, and 46 healthy controls (HC). Cognitive assessment utilized the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Resting-state functional magnetic resonance imaging (rs-fMRI) scans were conducted, and data were processed using fALFF in slow-4 and slow-5 bands. RESULTS: Significant fALFF changes were observed in four brain regions across MDD, SZDM, and HC groups for both slow-4 and slow-5 fALFF. Compared to SZDM, the MDD group showed increased slow-5 fALFF in the right gyrus rectus (RGR). Relative to HC, SZDM exhibited decreased slow-5 fALFF in the left gyrus rectus (LGR) and increased slow-5 fALFF in the right putamen. Changes in slow-5 fALFF in both RGR and LGR were negatively correlated with RBANS scores. No significant correlations were found between remaining fALFF (slow-4 and slow-5 bands) and RBANS scores in MDD or SZDM groups. CONCLUSIONS: Alterations in slow-5 fALFF in RGR may serve as potential biomarkers for distinguishing MDD from SZDM, providing preliminary insights into the neural mechanisms of cognitive function in schizophrenia.

The effects of cerebral pressure autoregulation status and CPP levels on cerebral metabolism in pediatric traumatic brain injury.

BACKGROUND: Cerebral perfusion pressure (CPP) management in the developing child with traumatic brain injury (TBI) is challenging. The pressure reactivity index (PRx) may serve as marker of cerebral pressure autoregulation (CPA) and optimal CPP (CPPopt) may be assessed by identifying the CPP level with best (lowest) PRx. To evaluate the potential of CPPopt guided management in children with severe TBI, cerebral microdialysis (CMD) monitoring levels of lactate and the lactate/pyruvate ratio (LPR) (indicators of ischemia) were related to actual CPP levels, autoregulatory state (PRx) and deviations from CPPopt (ΔCPPopt). METHODS: Retrospective study of 21 children ≤ 17 years with severe TBI who had both ICP and CMD monitoring were included. CPP, PRx, CPPopt and ΔCPPopt where calculated, dichotomized and compared with CMD lactate and lactate-pyruvate ratio. RESULTS: Median age was 16 years (range 8-17) and median Glasgow coma scale motor score 5 (range 2-5). Both lactate (p = 0.010) and LPR (p = < 0.001) were higher when CPP ≥ 70 mmHg than when CPP < 70. When PRx ≥ 0.1 both lactate and LPR were higher than when PRx < 0.1 (p = < 0.001). LPR was lower (p = 0.012) when CPPopt ≥ 70 mmHg than when CPPopt < 70, but there were no differences in lactate levels. When ΔCPPopt > 10 both lactate (p = 0.026) and LPR (p = 0.002) were higher than when ΔCPPopt < -10. CONCLUSIONS: Increased levels of CMD lactate and LPR in children with severe TBI appears to be related to disturbed CPA (PRx). Increased lactate and LPR also seems to be associated with actual CPP levels ≥ 70 mmHg. However, higher lactate and LPR values were also seen when actual CPP was above CPPopt. Higher CPP appears harmful when CPP is above the upper limit of pressure autoregulation. The findings indicate that CPPopt guided CPP management may have potential in pediatric TBI.

Salience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism.

Converging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network - an early-emerging neural network involved in orienting attention to the most salient aspects of one's internal and external environment - may predict the development of ASD symptoms such as reduced social attention and atypical sensory processing. Six-week-old infants at high likelihood of developing ASD based on family history exhibited stronger Salience Network connectivity with sensorimotor regions; infants at typical likelihood of developing ASD demonstrated stronger Salience Network connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in Salience Network connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with vulnerability to ASD.

Decreased Cerebral Perfusion in Chronic Migraine: A Voxel-based Cerebral Blood Flow Analysis Using 3D Pseudo-continuous Arterial Spin Labeling.

BACKGROUND: A contrast agent-free approach would be preferable to the frequently used invasive approaches for evaluating cerebral perfusion in chronic migraineurs (CM). In this work, non-invasive quantitative volumetric perfusion imaging was used to evaluate alterations in cerebral perfusion in CM. METHODS: We used conventional brain structural imaging sequences and 3D pseudo-continuous arterial spin labeling (3D PCASL) to examine thirteen CM patients and fifteen normal controls (NCs). The entire brain gray matter underwent voxel-based analysis, and the cerebral blood flow (CBF) values of the altered positive areas were retrieved to look into the clinical variables' significant correlation. RESULTS: Brain regions with the decreased perfusion were located in the left postcentral gyrus, bilateral middle frontal gyrus, left middle occipital gyrus, left superior parietal lobule, left medial segment of superior frontal gyrus, and right orbital part of the inferior frontal gyrus. White matter fibers with decreased perfusion were located in bilateral superior longitudinal tracts, superior corona radiata, external capsules, anterior and posterior limbs of the internal capsule, anterior corona radiata, inferior longitudinal fasciculus, and right corticospinal tract. However, the correlation analysis showed no significant correlation between the CBF value of the above positive brain regions with clinical variables (p > 0.05). CONCLUSION: The current study provided more useful information to comprehend the pathophysiology of CM and revealed a new insight into the neural mechanism of CM from the pattern of cerebral hypoperfusion.

Integration of Prior Expectations and Suppression of Prediction Errors During Expectancy-Induced Pain Modulation: The Influence of Anxiety and Pleasantness.

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating.

Altered intra- and inter-network brain functional connectivity associated with prolonged screen time in pre-school children with autism spectrum disorder.

Prolonged screen time (ST) has adverse effects on autistic characteristics and language development. However, the mechanisms underlying the effects of prolonged ST on the neurodevelopment of children with autism spectrum disorder (ASD) remain unclear. Neuroimaging technology may help to further explain the role of prolonged ST in individuals with ASD. This study included 164 cases, all cases were divided into low-dose ST exposure (LDE group 108 cases) and high-dose ST exposure (HDE group 56 cases) based on the average ST of all subjects. Spatial independent component analysis (ICA) was used to identify resting state networks (RSNs) and investigate intra- and inter-network alterations in ASD children with prolonged ST. We found that the total Childhood Autism Rating Scale (CARS) scores in the HDE group were significantly higher than those in the LDE group (36.2 ± 3.1 vs. 34.6 ± 3.9, p = 0.008). In addition, the developmental quotient (DQ) of hearing and language in the HDE group were significantly lower than those in the LDE group (31.5 ± 13.1 vs. 42.5 ± 18.5, p < 0.001). A total of 13 independent components (ICs) were identified. Between-group comparison revealed that the HDE group exhibited decreased functional connectivity (FC) in the left precuneus (PCUN) of the default mode network (DMN), the right middle temporal gyrus (MTG) of the executive control network (ECN), and the right median cingulate and paracingulate gyri (MCG) of the attention network (ATN), compared with the LDE group. Additionally, there was an increase in FC in the right orbital part of the middle frontal gyrus (ORBmid) of the salience network (SAN), compared with the LDE group. The inter-network analysis revealed increased FC between the visual network (VN) and basal ganglia (BG) and decreased FC between the sensorimotor network (SMN) and DMN, SMN and ATN, SMN and auditory network (AUN), and DMN and SAN in the HDE group, compared with the LDE group. There was a significant negative correlation between altered FC values in MTG and total CARS scores in subjects (r = - 0.18, p = 0.018).  Conclusion: ASD children with prolonged ST often exhibit lower DQ of language development and more severe autistic characteristics. The alteration of intra- and inter-network FC may be a key neuroimaging feature of the effect of prolonged ST on neurodevelopment in ASD children.  Clinical trial registration: ChiCTR2100051141. What is Known: • Prolonged ST has adverse effects on autistic characteristics and language development. • Neuroimaging technology may help to further explain the role of prolonged ST in ASD. What is New: • This is the first study to explore the impact of ST on intra- and inter-network FC in children with ASD. • ASD children with prolonged ST have atypical changes in intra- and inter-brain network FC.

The mind-skin connection: A narrative review exploring the link between inflammatory skin diseases and psychological stress.

Inflammatory skin diseases are known to negatively impact patient psychology, with individuals experiencing higher rates of stress and subsequent diminished quality of life, as well as mental health issues including anxiety and depression. Moreover, increased psychological stress has been found to exacerbate existing inflammatory skin diseases. The association between inflammatory skin diseases and psychological stress is a timely topic, and a framework to better understand the relationship between the two that integrates available literature is needed. In this narrative review article, we discuss potential neurobiological mechanisms behind psychological stress due to inflammatory skin diseases, focusing mainly on proinflammatory cytokines in the circulating system (the brain-gut-skin communications) and the default mode network in the brain. We also discuss potential descending pathways from the brain that lead to aggravation of inflammatory skin diseases due to psychological stress, including the central and peripheral hypothalamic-pituitary-adrenal axes, peripheral nerves and the skin barrier function.

Identification of the abnormalities in astrocytic functions as potential drug targets for neurodegenerative disease.

INTRODUCTION: Historically, astrocytes were seen primarily as a supportive cell population within the brain; with neurodegenerative disease research focusing exclusively on malfunctioning neurons. However, astrocytes perform numerous tasks that are essential for maintenance of the central nervous system`s complex processes. Disruption of these functions can have negative consequences; hence, it is unsurprising to observe a growing amount of evidence for the essential role of astrocytes in the development and progression of neurodegenerative diseases. Targeting astrocytic functions may serve as a potential disease-modifying drug therapy in the future. AREAS COVERED: The present review emphasizes the key astrocytic functions associated with neurodegenerative diseases and explores the possibility of pharmaceutical interventions to modify these processes. In addition, the authors provide an overview of current advancement in this field by including studies of possible drug candidates. EXPERT OPINION: Glial research has experienced a significant renaissance in the last quarter-century. Understanding how disease pathologies modify or are caused by astrocyte functions is crucial when developing treatments for brain diseases. Future research will focus on building advanced models that can more precisely correlate to the state in the human brain, with the goal of routinely testing therapies in these models.

Brain Mechanisms Explaining Postural Imbalance in Traumatic Brain Injury: A Systematic Review.

Introduction: Persisting imbalance and falls in community-dwelling traumatic brain injury (TBI) survivors are linked to reduced long-term survival. However, a detailed understanding of the impact of TBI upon the brain mechanisms mediating imbalance is lacking. To understand the state of the art concerning the brain mechanisms mediating imbalance in TBI, we performed a systematic review of the literature. Methods: PubMed, Web of Science, and Scopus were searched and peer-reviewed research articles in humans, with any severity of TBI (mild, moderate, severe, or concussion), which linked a postural balance assessment (objective or subjective) with brain imaging (through computed tomography, T1-weighted imaging, functional magnetic resonance imaging [fMRI], resting-state fMRI, diffusion tensor imaging, magnetic resonance spectroscopy, single-photon emission computed tomography, electroencephalography, magnetoencephalography, near-infrared spectroscopy, and evoked potentials) were included. Out of 1940 articles, 60 were retrieved and screened, and 25 articles fulfilling inclusion criteria were included. Results: The most consistent finding was the link between imbalance and the cerebellum; however, the regions within the cerebellum were inconsistent. Discussion: The lack of consistent findings could reflect that imbalance in TBI is due to a widespread brain network dysfunction, as opposed to focal cortical damage. The inconsistency in the reported findings may also be attributed to heterogeneity of methodology, including data analytical techniques, small sample sizes, and choice of control groups. Future studies should include a detailed clinical phenotyping of vestibular function in TBI patients to account for the confounding effect of peripheral vestibular disorders on imbalance and brain imaging.

Data-driven connectivity profiles relate to smoking cessation outcomes.

At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.

Dynamic analysis of amplitude of low-frequency fluctuation in children with growth hormone deficiency.

OBJECTIVE: Growth hormone (GH) affects brain activities and promotes growth and development. GH is a peptide hormone secreted by the anterior pituitary gland and is tied to behavior and cognitive function. Growth hormone deficiency (GHD) is the most common type of pathological short stature in children. Existing studies provide evidence that GHD may impact functional brain activities. The aim of this study was to investigate dynamic local brain activity in GHD children. METHOD: In this study, we combined amplitude of low-frequency fluctuations (ALFF) and sliding-window techniques to examine the local brain activity of children with GHD. The resting-state functional magnetic resonance imaging (fMRI) data were collected from 26 children with GHD and 15 age- and sex-matched healthy controls (HC). RESULT: Our results showed significant abnormal temporal variability of dynamic ALFF in widespread regions in children with GHD, primarily in the frontal gyrus, temporal gyrus, and parietal lobule. CONCLUSION: The dALFF can capture dynamic changes in brain spontaneous activity, which are related to behavior and cognition. Based on this dynamic local brain activity, the results of this study provide a better understanding of the pathophysiological mechanism in children with GHD.

Functional neuroimaging as a catalyst for integrated neuroscience.

Functional magnetic resonance imaging (fMRI) enables non-invasive access to the awake, behaving human brain. By tracking whole-brain signals across a diverse range of cognitive and behavioural states or mapping differences associated with specific traits or clinical conditions, fMRI has advanced our understanding of brain function and its links to both normal and atypical behaviour. Despite this headway, progress in human cognitive neuroscience that uses fMRI has been relatively isolated from rapid advances in other subdomains of neuroscience, which themselves are also somewhat siloed from one another. In this Perspective, we argue that fMRI is well-placed to integrate the diverse subfields of systems, cognitive, computational and clinical neuroscience. We first summarize the strengths and weaknesses of fMRI as an imaging tool, then highlight examples of studies that have successfully used fMRI in each subdomain of neuroscience. We then provide a roadmap for the future advances that will be needed to realize this integrative vision. In this way, we hope to demonstrate how fMRI can help usher in a new era of interdisciplinary coherence in neuroscience.