Resting-state Functional Imaging Research Articles

Coupled pulsatile vascular and paravascular fluid dynamics in the human brain

BackgroundCardiac pulsation propels blood through the cerebrovascular network to maintain cerebral homeostasis. The cerebrovascular network is uniquely surrounded by paravascular cerebrospinal fluid (pCSF), which plays a crucial role in waste removal, and its flow is suspected to be driven by arterial pulsations. Despite its importance, the relationship between vascular and paravascular fluid dynamics throughout the cardiac cycle remains poorly understood in humans.MethodsIn this study, we developed a non-invasive neuroimaging approach to investigate the coupling between pulsatile vascular and pCSF dynamics within the subarachnoid space of the human brain. Resting-state functional MRI (fMRI) and dynamic diffusion-weighted imaging (dynDWI) were retrospectively cardiac-aligned to represent cerebral hemodynamics and pCSF motion, respectively. We measured the time between peaks (∆TTP) in ddϕfMRI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\frac{d}{d\\phi }fMRI$$\\end{document} and dynDWI waveforms and measured their coupling by calculating the waveforms correlation after peak alignment (correlation at aligned peaks). We compared the ∆TTP and correlation at aligned peaks between younger [mean age: 27.9 (3.3) years, n = 9] and older adults [mean age: 70.5 (6.6) years, n = 20], and assessed their reproducibility within subjects and across different imaging protocols.ResultsHemodynamic changes consistently precede pCSF motion. ∆TTP was significantly shorter in younger adults compared to older adults (−0.015 vs. −0.069, p < 0.05). The correlation at aligned peaks were high and did not differ between younger and older adults (0.833 vs. 0.776, p = 0.153). The ∆TTP and correlation at aligned peaks were robust across fMRI protocols (∆TTP: −0.15 vs. −0.053, p = 0.239; correlation at aligned peaks: 0.813 vs. 0.812, p = 0.985) and demonstrated good to excellent within-subject reproducibility (∆TTP: intraclass correlation coefficient = 0.36; correlation at aligned peaks: intraclass correlation coefficient = 0.89).ConclusionThis study proposes a non-invasive technique to evaluate vascular and paravascular fluid dynamics. Our findings reveal a consistent and robust cardiac pulsation-driven coupling between cerebral hemodynamics and pCSF dynamics in both younger and older adults.

Brain topology underlying executive functions across the lifespan: focus on the default mode network.

While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner. Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalized on graph theory to chart cross-sectional variations in the intrinsic functional organization of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan. Topological properties of both the FPN and DMN were associated with EF performance but not with a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioral efficiency within the EF domain. The topological organization of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN. The DMN matures earlier in life than the FPN and it ıs more susceptible to neurodegenerative changes. Because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought.

Eight brain structures mediate the age-related alterations of the working memory: forward and backward digit span tasks.

Working memory (WM) as one of the executive functions is an essential neurocognitive ability for daily life. Findings have suggested that aging is often associated with working memory and neural decline, but the brain structures and resting-state brain networks that mediate age-related differences in WM remain unclear. A sample consisting of 252 healthy participants in the age range of 20 to 70years was used. Several cognitive tasks, including the n-back task and the forward and backward digit span tests were used. Also, resting-state functional imaging, as well as structural imaging using a 3T MRI scanner, were performed, resulting in 85 gray matter volumes and five resting-state networks, namely the anterior and posterior default mode, the right and left executive control, and the salience networks. Also, mediation analyses were used to investigate the role of gray matter volumes and resting-state networks in the relationship between age and WM. Behaviorally, aging was associated with decreased performance in the digit span task. Also, aging was associated with a decreased gray matter volume in 80 brain regions, and with a decreased activity in the anterior default mode network, executive control, and salience networks. Importantly, the path analysis showed that the GMV of the medial orbitofrontal, precentral, parieto-occipital, amygdala, middle occipital, posterior cingulate, and thalamus areas mediated the age-related differences in the forward digit span task, and the GMV of superior temporal gyrus mediated the age-related differences in the backward digit span task. This study identified the brain structures mediating the relationship between age and working memory, and we hope that our research provides an opportunity for early detection of individuals at risk of age-related memory decline.

Microstructural and functional substrates underlying dispositional greed and its link with trait but not state impulsivity

The interplay between personality traits and impulsivity has long been a central theme in psychology and psychiatry. However, the potential association between Greed Personality Traits (GPT) and impulsivity, encompassing both trait and state impulsivity and future time perspective, remains largely unexplored. To address these issues, we employed questionnaires and an inter-temporal choice task to estimate corresponding trait/state impulsivity and collected multi-modal neuroimaging data (resting-state functional imaging: n = 430; diffusion-weighted imaging: n = 426; task-related functional imaging: n = 53) to investigate the underlying microstructural and functional substrates. Behavioral analyses revealed that GPT mediated the association between time perspective (e.g., present fatalism) and trait impulsivity (e.g., motor impulsivity). Functional imaging analyses further identified that brain activation strengths and patterns related to delay length, particularly in the dorsomedial prefrontal cortex, superior parietal lobule, and cerebellum, were associated with GPT. Moreover, individuals with similar levels of greed exhibited analogous spontaneous brain activity patterns, predominantly in the Default Mode Network (DMN), Fronto-Parietal Network (FPN), and Visual Network (VIS). Diffusion imaging analysis observed specific microstructural characteristics in the spinocerebellar/pontocerebellar fasciculus, internal/external capsule, and corona radiata that support the formation of GPT. Furthermore, the corresponding neural activation pattern, spontaneous neural activity pattern, and analogous functional couplings among the aforementioned brain regions mediated the relationships between time perspective and GPT and between GPT and motor impulsivity. These findings provide novel insights into the possible pathway such as time perspective → dispositional greed → impulsivity and uncover their underlying microstructural and functional substrates.

Similarity and characterization of structural and functional neural connections within species under isoflurane anesthesia in the common marmoset

The common marmoset is an essential model for understanding social cognition and neurodegenerative diseases. This study explored the structural and functional brain connectivity in a marmoset under isoflurane anesthesia, aiming to statistically overcome the effects of high inter-individual variability and noise-related confounds such as physiological noise, ensuring robust and reliable data. Similarities and differences in individual subject data, including assessments of functional and structural brain connectivities derived from resting-state functional MRI and diffusion tensor imaging were meticulously captured. The findings highlighted the high consistency of structural neural connections within the species, indicating a stable neural architecture, while functional connectivity under anesthesia displayed considerable variability. Through independent component and dual regression analyses, several distinct brain connectivities were identified, elucidating their characteristics under anesthesia. Insights into the structural and functional features of the marmoset brain from this study affirm its value as a neuroscience research model, promising advancements in the field through fundamental and translational studies.

Gender differences in major depressive disorder at different ages: a REST-meta-MDD project-based study

BackgroundMajor depressive disorder (MDD) is a highly heterogeneous disease, with differences in clinical manifestations among depression patients based on onset ages and genders. The neural mechanisms underlying these differences remain unclear. In this study, we utilized resting state functional imaging data from a large sample database and adopted the ReHo method to investigate gender differences in local brain function in MDD patients across different onset age groups.MethodsThe study included 364 MDD patients and 695 healthy participants who were part of the REST-meta-MDD project. Regional homogeneity (ReHo) assessed gender disparities in MDD and healthy individuals within groups delineated by gender and onset age (young group: 18–29 years; middle-aged group: 30–45 years).ResultsAmong the young MDD groups, there were significant gender differences in the right superior frontal gyrus, right inferior frontal gyrus, left superior temporal gyrus, and right superior parietal lobule, with male MDD patients having higher ReHo values compared to females. When compared to healthy males, male MDD patients exhibited elevated ReHo values in the right superior parietal lobule. In the middle-aged groups, a marked ReHo difference was observed in the bilateral cerebellum posterior lobe, with female MDD patients showing higher ReHo values.ConclusionsThe functional mechanisms of MDD differ between genders and show distinct variations across different onset age groups. These findings underscore the importance of developing personalized interventions that address the unique needs of MDD patients, tailored to their gender and age, and necessitate the development of antidepressant medications targeted at each gender-age subgroup.

Moving beyond processing- and analysis-related variation in resting-state functional brain imaging.

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation.

Feasibility and success of a non-sedated brain MRI training protocol in 7-year-old children from rural and semi-rural Colombia.

Brain magnetic resonance imaging (MRI) is a crucial tool for clinical evaluation of the brain and neuroscience research. Obtaining successful non-sedated MRI in children who live in resource-limited settings may be an additional challenge. To present a feasibility study of a novel, low-cost MRI training protocol used in a clinical research study in a rural/semi-rural region of Colombia and to examine neurodevelopmental factors associated with successful scans. Fifty-seven typically developing Colombian children underwent a training protocol and non-sedated brain MRI at age 7. Group training utilized a customized booklet, an MRI toy set, and a simple mock scanner. Children attended MRI visits in small groups of two to three. Resting-state functional and structural images were acquired on a 1.5-Tesla scanner with a protocol duration of 30-40minutes. MRI success was defined as the completion of all sequences and no more than mild motion artifact. Associations between the Wechsler Preschool and Primary Scale of Intelligence (WPPSI), Movement Assessment Battery for Children (MABC), Behavioral Rating Inventory of Executive Function (BRIEF), Child Behavior Checklist (CBCL), and Adaptive Behavior Assessment System (ABAS) scores and MRI success were analyzed. Mean (SD) age at first MRI attempt was 7.2 (0.2) years (median 7.2 years, interquartile range 7.1-7.3 years). Twenty-six (45.6%) participants were male. Fifty-one (89.5%) children were successful across two attempts; 44 (77.2%) were successful on their first attempt. Six (10.5%) were unsuccessful due to refusal or excessive motion. Age, sex, and scores across all neurodevelopmental assessments (MABC, TVIP, ABAS, BRIEF, CBCL, NIH Toolbox Flanker, NIH Toolbox Pattern Comparison, WPPSI) were not associated with likelihood of MRI success (P=0.18, 0.19, 0.38, 0.92, 0.84, 0.80, 1.00, 0.16, 0.75, 0.86, respectively). This cohort of children from a rural/semi-rural region of Colombia demonstrated comparable MRI success rates to other published cohorts after completing a low-cost MRI familiarization training protocol suitable for low-resource settings. Achieving non-sedated MRI success in children in low-resource and international settings is important for the continuing diversification of pediatric research studies.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

Resting-State Functional Connectivity Predicts Cochlear-Implant Speech Outcomes.

Cochlear implants (CIs) have revolutionized hearing restoration for individuals with severe or profound hearing loss. However, a substantial and unexplained variability persists in CI outcomes, even when considering subject-specific factors such as age and the duration of deafness. In a pioneering study, we use resting-state functional near-infrared spectroscopy to predict speech-understanding outcomes before and after CI implantation. Our hypothesis centers on resting-state functional connectivity (FC) reflecting brain plasticity post-hearing loss and implantation, specifically targeting the average clustering coefficient in resting FC networks to capture variation among CI users. Twenty-three CI candidates participated in this study. Resting-state functional near-infrared spectroscopy data were collected preimplantation and at 1 month, 3 months, and 1 year postimplantation. Speech understanding performance was assessed using consonant-nucleus-consonant words in quiet and Bamford-Kowal-Bench sentences in noise 1-year postimplantation. Resting-state FC networks were constructed using regularized partial correlation, and the average clustering coefficient was measured in the signed weighted networks as a predictive measure for implantation outcomes. Our findings demonstrate a significant correlation between the average clustering coefficient in resting-state functional networks and speech understanding outcomes, both pre- and postimplantation. This approach uses an easily deployable resting-state functional brain imaging metric to predict speech-understanding outcomes in implant recipients. The results indicate that the average clustering coefficient, both pre- and postimplantation, correlates with speech understanding outcomes.

Treatment outcomes in tinnitus patients are associated with brain functional network: Evidence from connectome gradient and gene expression analysis

The neuroimaging mechanisms underlying differences in the outcomes of sound therapy for tinnitus patients remain unclear. We hypothesize that abnormal hierarchical architecture is the neuro-biomarker for treatment outcome explanation. We conducted functional connectome gradient analyses on resting-state functional MRI images that acquired before intervention to investigate differences among the patients with effective treatment (ET, n = 27), ineffective treatment (IT, n = 41), and healthy controls (HC, n = 59). General linear models were used to analyze the associations between intergroup differential regions and clinical characteristics. Partial least squares regression was employed to reveal correlations with gene expression. Compared to HC, both ET and IT groups displayed significant differences in the default mode network. Moreover, the ET group exhibited wider gradient range and greater gradient variance. Also, the gradient scores of the differential regions between the ET and HC groups were significantly correlated with Self-rating Anxiety Scale and Self-rating Depression Scale scores, and exhibited positive correlations with the transcriptional profiles of genes related to depression and anxiety. Our results indicated that the abnormalities of ET group, may be more relevant to psychiatric disorders, bringing a higher possible therapeutic potential due to the plasticity of the nervous system. Connectome gradient dysfunction with genetic evidence may serve as an indicator for identifying diverse treatment outcomes of the sound therapy for tinnitus patients before treatment.

The impact of illness duration on brain activity in goal-directed and habit-learning systems in obsessive-compulsive disorder progression: A resting-state functional imaging study

It is increasingly evident that structural and functional changes in brain regions associated with obsessive–compulsive disorder (OCD) are often related to the development of the disease. However, limited research has been conducted on how the progression of OCD may lead to an imbalance between goal-directed and habit-learning systems. This study employs resting-state functional imaging to examine the relationship between illness duration and abnormal brain function in goal-directed/habitual-learning systems. Demographic, clinical, and multimodal fMRI data were collected from participants. Our findings suggest that, compared to healthy controls, individuals with OCD exhibit abnormal brain functional indicators in both goal-directed and habit-learning brain regions, with a more pronounced reduction observed in the goal-directed regions. Additionally, abnormal brain activity is associated with illness duration, and the abnormalities observed in goal-directed regions are more effective in distinguishing different courses of OCD patients. Patients with different durations of OCD have functional abnormalities in the goal-directed and habitual-learning brain regions. There are differences in the degree of abnormality in different brain regions, and these abnormalities may disrupt the balance between goal-directed and habitual-learning systems, leading to increasing reliance on repetitive behaviors.

NREM sleep brain networks modulate cognitive recovery from sleep deprivation.

Decrease in cognitive performance after sleep deprivation followed by recovery after sleep suggests its key role, and especially non-rapid eye movement (NREM) sleep, in the maintenance of cognition. It remains unknown whether brain network reorganization in NREM sleep stages N2 and N3 can uniquely be mapped onto individual differences in cognitive performance after a recovery nap following sleep deprivation. Using resting state functional magnetic resonance imaging (fMRI), we quantified the integration and segregation of brain networks during NREM sleep stages N2 and N3 while participants took a 1-hour nap following 24-hour sleep deprivation, compared to well-rested wakefulness. Here, we advance a new analytic framework called the hierarchical segregation index (HSI) to quantify network segregation across spatial scales, from whole-brain to the voxel level, by identifying spatio-temporally overlapping large-scale networks and the corresponding voxel-to-region hierarchy. Our results show that network segregation increased in the default mode, dorsal attention and somatomotor networks during NREM sleep compared to wakefulness. Segregation within the visual, limbic, and executive control networks exhibited N2 versus N3 sleep-specific voxel-level patterns. More segregation during N3 was associated with worse recovery of working memory, executive attention, and psychomotor vigilance after the nap. The level of spatial resolution of network segregation varied among brain regions and was associated with the recovery of performance in distinct cognitive tasks. We demonstrated the sensitivity and reliability of voxel-level HSI to provide key insights into within-region variation, suggesting a mechanistic understanding of how NREM sleep replenishes cognition after sleep deprivation.

Atypical intrinsic neural timescale in the left angular gyrus in Alzheimer's disease.

Alzheimer's disease is characterized by cognitive impairment and progressive brain atrophy. Recent human neuroimaging studies reported atypical anatomical and functional changes in some regions in the default mode network in patients with Alzheimer's disease, but which brain area of the default mode network is the key region whose atrophy disturbs the entire network activity and consequently contributes to the symptoms of the disease remains unidentified. Here, in this case-control study, we aimed to identify crucial neural regions that mediated the phenotype of Alzheimer's disease, and as such, we examined the intrinsic neural timescales-a functional metric to evaluate the capacity to integrate diverse neural information-and grey matter volume of the regions in the default mode network using resting-state functional MRI images and structural MRI data obtained from individuals with Alzheimer's disease and cognitively typical people. After confirming the atypically short neural timescale of the entire default mode network in Alzheimer's disease and its link with the symptoms of the disease, we found that the shortened neural timescale of the default mode network was associated with the aberrantly short neural timescale of the left angular gyrus. Moreover, we revealed that the shortened neural timescale of the angular gyrus was correlated with the atypically reduced grey matter volume of this parietal region. Furthermore, we identified an association between the neural structure, brain function and symptoms and proposed a model in which the reduced grey matter volume of the left angular gyrus shortened the intrinsic neural time of the region, which then destabilized the entire neural timescale of the default mode network and resultantly contributed to cognitive decline in Alzheimer's disease. These findings highlight the key role of the left angular gyrus in the anatomical and functional aetiology of Alzheimer's disease.

Preventive effect of one-session brief focused attention meditation on state fatigue: Resting state functional magnetic resonance imaging study

IntroductionThe extended practice of meditation may reduce the influence of state fatigue by changing neurocognitive processing. However, little is known about the preventive effects of one-session brief focused attention meditation (FAM) on state fatigue in healthy participants or its potential neural mechanisms. This study examined the preventive effects of one-session brief FAM on state fatigue and its neural correlates using resting-state functional MRI (rsfMRI) measurements. MethodsWe randomly divided 56 meditation-naïve participants into FAM and control groups. After the first rsfMRI scan, each group performed a 10-minute each condition while wearing a functional near-infrared spectroscopy (fNIRS) device for assessing brain activity. Subsequently, following a second rsfMRI scan, the participants completed a fatigue-inducing task (a Go/NoGo task) for 60 min. We evaluated the temporal changes in the Go/NoGo task performance of participants as an indicator of state fatigue. We then calculated changes in the resting-state functional connectivity (rsFC) of the rsfMRI from before to after each condition and compared them between groups. We also evaluated neural correlates between the changes in rsFC and state fatigue. Results and discussionThe fNIRS measurements indicated differences in brain activity during each condition between the FAM and control groups, showing decreased medial prefrontal cortex activity and decreased functional connectivity between the medial prefrontal cortex and middle frontal gyrus. The control group exhibited a decrement in Go/NoGo task performance over time, whereas the FAM group did not. These results, thus, suggested that FAM could prevent state fatigue. Compared with the control group, the rsFC analysis revealed a significant increase in the connectivity between the left dorsomedial prefrontal cortex and right superior parietal lobule in the FAM group, suggesting a modification of attention regulation by cognitive effort. In the control group, increased connectivity was observed between the bilateral posterior cingulate cortex and left inferior occipital gyrus, which might be associated with poor attention regulation and reduced higher-order cognitive function. Additionally, the change in the rsFC of the control group was related to state fatigue. ConclusionOur findings suggested that one session of 10-minute FAM could prevent behavioral state fatigue by employing cognitive effort to modify attention regulation as well as suppressing poor attention regulation and reduced higher-order cognitive function.

Altered intra- and inter-network connectivity in autism spectrum disorder.

A neurodevelopmental illness termed as the autism spectrum disorder (ASD) is described by social interaction impairments. Previous studies employing resting-state functional imaging (rs-fMRI) identified both hyperconnectivity and hypoconnectivity patterns in ASD people. However, specific patterns of connectivity within and between networks linked to ASD remain largely unexplored. We utilized a meticulously selected subset of high-quality data, comprising 45 individuals diagnosed with ASD and 47 HCs, obtained from the ABIDE dataset. The pre-processed rs-fMRI time series signals were partitioned into ninety regions of interest. We focused on eight intrinsic connectivity networks and further performed intra- and inter-network analysis. Finally, support vector machine was used to discriminate ASD from HC. Through different sparsities, ASD exhibited significantly decreased intra-network connectivity within default mode network and dorsal attention network, increased connectivity between limbic network and subcortical network, and decreased connectivity between default mode network and limbic network. Using the classifier trained on altered intra- and inter-network connectivity, multivariate pattern analyses classified the ASD from HC with 71.74% accuracy, 70.21% specificity and 75.56% sensitivity in 10% sparsity of functional connectivity. ASD showed characteristic reorganization of the brain networks and this provided new insight into the underlying process of the functional connectome dysfunction in ASD.

An investigation of the neural basis of anger attributions in irritable youth.

Neurocognitive models of pediatric irritability suggest a prominent role of anger; however, few studies have investigated anger-related biases and their neural correlates. Resting state functional connectivity (rsFC) of the amygdala was examined in relation to anger attribution bias (AAB) in a sample of young children (5-9 years old; N = 60; 55% White, 26.7% Hispanic) with clinically significant irritability characterized by impairing emotional outbursts (IEOs). Children completed a resting state functional magnetic resonance imaging scan as well as the assessment of children's emotional skills (ACES), which yields three measures of AAB in the context of social situations, social behaviors, and facial expressions. ACES scores were entered into a general linear model to examine associations with rsFC of the bilateral amygdalae. Children with IEOs exhibited significant biases in attributing anger to others across all three ACES domains. Greater biases toward attributing anger in social situations were associated with reduced rsFC of the bilateral amygdalae with the fusiform/lingual gyri and lateral occipital cortex. Alternatively, greater biases toward attributing anger to facial expressions positively predicted right amygdala-precuneus rsFC. Greater bias toward attributing anger to others based on their behaviors was associated with heightened rsFC of the right amygdala with the left middle frontal gyrus. Findings extend previous work implicating functional connections among regions of default mode and frontoparietal networks in pediatric irritability. Longitudinal studies are needed to further investigate the putative role of AAB in the etiology and long-term outcomes of pediatric irritability. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Brain structural and functional abnormalities associated with acute post-traumatic headache: iron deposition and functional connectivity

BackgroundThe purpose of this study was to interrogate brain iron accumulation in participants with acute post-traumatic headache (PTH) due to mild traumatic brain injury (mTBI), and to determine if functional connectivity is affected in areas with iron accumulation. We aimed to examine the correlations between iron accumulation and headache frequency, post-concussion symptom severity, number of mTBIs, and time since most recent TBI.MethodsSixty participants with acute PTH and 60 age-matched healthy controls (HC) underwent 3T magnetic resonance imaging including quantitative T2* maps and resting-state functional connectivity imaging. Between group T2* differences were determined using T-tests (p < 0.005, cluster size threshold of 90 voxels). For regions with T2* differences, two analyses were conducted. First, the correlations with clinical variables including headache frequency, number of lifetime mTBIs, time since most recent mTBI, and Sport Concussion Assessment Tool (SCAT) symptom severity scale scores were investigated using linear regression. Second, the functional connectivity of these regions with the rest of the brain was examined (significance of p < 0.05 with family wise error correction for multiple comparisons).ResultsThe acute PTH group consisted of 60 participants (22 male, 38 female) with average age of 42 ± 14 years. The HC group consisted of 60 age-matched controls (17 male, 43 female, average age of 42 ± 13). PTH participants had lower T2* values compared to HC in the left posterior cingulate and the bilateral cuneus. Stronger functional connectivity was observed between bilateral cuneus and right cerebellar areas in PTH compared to HC. Within the PTH group, linear regression showed negative associations of T2* in the left posterior cingulate with SCAT symptom severity score (p = 0.05) and T2* in the left cuneus with headache frequency (p = 0.04).ConclusionsIron accumulation in posterior cingulate and cuneus was observed in those with acute PTH relative to HC; stronger functional connectivity was detected between the bilateral cuneus and the right cerebellum. The correlations of decreased T2* (suggesting higher iron content) with headache frequency and post mTBI symptom severity suggest that the iron accumulation that results from mTBI might reflect the severity of underlying mTBI pathophysiology and associate with post-mTBI symptom severity including PTH.

Abnormal intrinsic functional hubs and connectivity in patients with post-stroke depression.

The present study aimed to investigate the specific alterations of brain networks in patients with post-stroke depression (PSD), and further assist in elucidating the brain mechanisms underlying the PSD which would provide supporting evidence for early diagnosis and interventions for the disease. Resting-state functional magnetic resonace imaging data were acquired from 82 nondepressed stroke patients (Stroke), 39 PSD patients, and 74 healthy controls (HC). Voxel-wise degree centrality (DC) conjoined with seed-based functional connectivity (FC) analyses were performed to investigate the PSD-related connectivity alterations. The relationship between these alterations and depression severity was further examined in PSD patients. Relative to both Stroke and HC groups, (1) PSD showed increased centrality in regions within the default mode network (DMN), including contralesional angular gyrus (ANG), posterior cingulate cortex (PCC), and hippocampus (HIP). DC values in contralesional ANG positively correlated with the Patient Health Questionnaire-9 (PHQ-9) scores in PSD group. (2) PSD exhibited increased connectivity between these three seeds showing altered DC and regions within the DMN: bilateral medial prefrontal cortex and middle temporal gyrus and ipsilesional superior parietal gyrus, and regions outside the DMN: bilateral calcarine, ipsilesional inferior occipital gyrus and contralesional lingual gyrus, while decreased connectivity between contralesional ANG and contralesional supramarginal gyrus. Moreover, these FC alterations could predict PHQ-9 scores in PSD group. These findings highlight that PSD was related with increased functional connectivity strength in some areas within the DMN, which might be attribute to the specific alterations of connectivity between within DMN and outside DMN regions in PSD.

Multimodal fusion of brain signals for robust prediction of psychosis transition

The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.