Most recent paper

BMC Psychiatry. 2024 Nov 20;24(1):837. doi: 10.1186/s12888-024-06291-0.

ABSTRACT

OBJECTIVE: State anxiety and trait anxiety are differentially mapped in brain function. However, the genetic and neurobiological basis of anxiety-related functional changes remain largely unknown.

METHODS: Participants aged 18-30 from the community underwent resting-state fMRI and were assessed with the State-Trait Anxiety Inventory. Using a general linear regression model, we analyzed the effects of state and trait anxiety, as well as their sum and difference (delta), on regional homogeneity (ReHo) in cortical areas. ReHo patterns denote the spatial distribution of ReHo associated with anxiety scores. We further explored the spatial correlations between ReHo patterns and neuromaps, including gene expression, neurotransmitter receptor density, myelination, and functional connectivity gradients, to elucidate the genetic and molecular substrates of these ReHo patterns.

RESULTS: Our findings demonstrated robust spatial correlations between whole-brain ReHo patterns for state and trait anxiety, with trait anxiety and the delta value exhibiting stronger network correlations, notably in the dorsal attention, salience, visual, and sensorimotor networks. Genes highly correlated with ReHo patterns exhibited unique spatiotemporal expression patterns, involvement in oxidative stress, metabolism, and response to stimuli, and were expressed in specific cell types. Furthermore, ReHo patterns significantly correlated with neuromaps of neurotransmitter receptor density, myelination, and functional connectivity gradients.

CONCLUSIONS: The ReHo patterns associated with anxiety may be driven by genetic and neurobiological traits. Our findings contribute to a deeper understanding of the pathogenesis of anxiety from a genetic and molecular perspective.

PMID:39567951 | PMC:PMC11577826 | DOI:10.1186/s12888-024-06291-0

Sci Rep. 2024 Nov 21;14(1):28809. doi: 10.1038/s41598-024-80216-5.

ABSTRACT

Diabetic peripheral neuropathy (DPN) is the most common complication of type 2 diabetes mellitus (T2DM) and is often accompanied by a variety of cognitive and emotional deficits, but the neurologic mechanisms underlying these deficits have not been fully elucidated. Therefore, this study aimed to use independent component analysis to explore the changes in the characteristics within the intrinsic network and to reveal patterns of interactions between networks in patients with DPN. Forty-one patients with T2DM who showed DPN, 37 patients with T2DM who did not show DPN (NDPN group), and 43 healthy controls (HC) underwent a neuropsychological assessment and resting-state functional magnetic resonance imaging examinations to examine the patterns of intra- and inter-network variations in the patients with T2DM at different clinical stages (with and without DPN). The relationships of intra- and inter-network functional connectivity (FC) with clinical/cognitive variables were also examined. In comparison with the NDPN group and HC, patients with DPN showed decreased FC within the visual network and sensorimotor network (SMN). Moreover, in comparison with the HC group, patients with DPN showed decreased FC within the anterior default mode network and increased FC within the basal ganglia network. Inter-network analysis showed decreased FC between the SMN and salience network in patients with DPN relative to the NDPN and HC groups. The decreased FC within the bilateral paracentral lobule (BA 6) of SMN was associated with Color Trails Test part 1 scores (r = -0.302, P = 0.007) and disease duration (r = -0.328, P = 0.003) in all patients with T2DM. In conclusion, the results revealed that patients with DPN have abnormal FC in multiple resting-state intrinsic networks in addition to the SMN, and that decreased FC between the SMN and salience network may be involved in the neural basis of abnormal sensorimotor function in patients with DPN.

PMID:39567712 | PMC:PMC11579012 | DOI:10.1038/s41598-024-80216-5

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Nov 18:S2451-9022(24)00340-9. doi: 10.1016/j.bpsc.2024.11.003. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescence is a time of increased risk for the onset of internalising problems, particularly in females. However, how individual differences in brain maturation relate to the increased vulnerability for internalising problems in adolescence remains poorly understood due to a scarcity of longitudinal studies.

METHODS: Using Adolescent Brain Cognitive Development (ABCD) Study data, we examined longitudinal associations between multimodal brain age and youth internalising problems. Brain age models were trained, validated, and tested independently on T1-weighted (T1; N=9523), diffusion tensor (DTI; N=8834), and resting-state functional (rs-fMRI; N=8233) MRI data at baseline (Mage= 9.9 years) and 2-year follow-up (Mage= 11.9 years). Self-reported internalising problems were measured at 3-year follow-up (Mage= 12.9 years) using the Brief Problem Monitor.

RESULTS: Latent change score models demonstrated that although brain age gap (BAG) at baseline was not related to later internalising problems, an increase in BAG between timepoints was positively associated with internalising problems at 3-year follow-up in females but not males. This association between an increasing BAG and higher internalising problems was observed in the T1 (β = 0.067, SE = 0.050, pFDR = 0.020) and rs-fMRI β = 0.090, SE = 0.025, pFDR = 0.007) models but not DTI (β=-0.002, SE=0.053, pFDR = 0.932), and remained significant when accounting for earlier internalising problems.

CONCLUSIONS: A greater increase in BAG in early adolescence may reflect the heightened vulnerability shown by female youth to internalising problems. Longitudinal research is necessary to understand if this increasing BAG signifies accelerated brain development and its relationship to the trajectory of internalising problems throughout adolescence.

PMID:39566883 | DOI:10.1016/j.bpsc.2024.11.003

J Affect Disord. 2024 Nov 18:S0165-0327(24)01868-8. doi: 10.1016/j.jad.2024.11.035. Online ahead of print.

ABSTRACT

Objective and quantitative neuroimaging biomarkers are crucial for early diagnosis of major depressive disorder (MDD). However, previous studies using machine learning (ML) to distinguish MDD have often used small sample sizes and overlooked MDD's neural connectome and mechanism. To address these gaps, we applied Dynamic Graph Convolutional Nets (DGCNs) to a large multi-site dataset of 2317 resting state functional MRI (RS-fMRI) scans from 1081 MDD patients and 1236 healthy controls from 16 Rest-meta-MDD consortium sites. Our DGCN model combined with the personal whole brain functional connectivity (FC) network achieved an accuracy of 82.5 % (95 % CI:81.6-83.4 %, AUC:0.869), outperforming other universal ML classifiers. The most prominent domains for classification were mainly in the default mode network, fronto-parietal and cingulo-opercular network. Our study supports the stability and efficacy of using DGCN to characterize MDD and demonstrates its potential in enhancing neurobiological comprehension of MDD by detecting clinically related disorders in FC network topologies.

PMID:39566747 | DOI:10.1016/j.jad.2024.11.035

Ageing Res Rev. 2024 Nov 18:102590. doi: 10.1016/j.arr.2024.102590. Online ahead of print.

ABSTRACT

INTRODUCTION: Functional brain connectivity of resting-state networks varies as Alzheimer's disease (AD) progresses. However, our understanding of the dynamic longitudinal changes that occur in the brain over the course of AD is sometimes contradictory and lacking.

MATERIALS AND METHODS: In this study, we analyzed whole-brain networks connectivity using longitudinal resting-state fMRI data from 132 participants from ADNI dataset. The cohort was divided into four groups: 20 AD, 35 CN, 46 Early MCI, and 31 Late MCI Cross-sectional analyses were conducted at baseline and follow-up (approximately two years apart), with longitudinal changes assessed within and between groups.

RESULTS: Cross-sectional analyses revealed that all groups differed significantly from AD in global network properties at both time points, with EMCI also showing disrupted topological metrics compared to CN. Longitudinal analyses highlighted notable changes in small-worldness (σ), global clustering coefficient (Cp), and normalized characteristic path length (λ) across groups. Both EMCI and LMCI groups showed significant alterations in global efficiency (Eglob), Cp, and σ over time. Pairwise comparisons also revealed significant interaction effects, particularly between CN-EMCI and CN-AD groups. All groups showed notable changes in σ, λ, and Cp, according to within-group longitudinal changes. Furthermore, distinct changes in Eglob over time were observed in the LMCI and EMCI groups. Almost all subnetwork attributes demonstrated significant changes between patients at various phases in both time intervals.

CONCLUSION: Our findings emphasize significant connectivity alterations across all groups at both baseline and follow-up, with longitudinal analyses underscoring the progression of these changes. Graph theory metrics provide valuable insights into the transition from normal cognition to AD, potentially serving as biomarkers for disease progression.

PMID:39566740 | DOI:10.1016/j.arr.2024.102590

Cortex. 2024 Nov 8;181:216-232. doi: 10.1016/j.cortex.2024.08.012. Online ahead of print.

ABSTRACT

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

PMID:39566125 | DOI:10.1016/j.cortex.2024.08.012

Front Neurosci. 2024 Nov 5;18:1515904. doi: 10.3389/fnins.2024.1515904. eCollection 2024.

ABSTRACT

[This corrects the article DOI: 10.3389/fnins.2021.823550.].

PMID:39564527 | PMC:PMC11574553 | DOI:10.3389/fnins.2024.1515904

Adv Neurobiol. 2024;40:511-544. doi: 10.1007/978-3-031-69491-2_18.

ABSTRACT

The promise of individually tailored care for autism has driven efforts to establish biomarkers. This chapter appraises the state of precision-medicine research focused on biomarkers based on the functional brain connectome. This work is grounded on abundant evidence supporting the brain dysconnection model of autism and the advantages of resting-state functional MRI (R-fMRI) for studying the brain in vivo. After considering biomarker requirements of consistency and clinical relevance, we provide a scoping review of R-fMRI studies of individual prediction in autism. In the past 10 years, responding to the availability of open data through the Autism Brain Imaging Data Exchange, machine learning studies have surged. Nearly all have focused on diagnostic label classification. These efforts have shown that autism prediction is feasible using functional connectome markers, with accuracy reported well above chance. In parallel, emerging approaches more directly addressing autism heterogeneity are paving the way for much-needed biomarkers of longitudinal outcome and treatment response. We conclude with key challenges to be addressed by the next generation of studies.

PMID:39562456 | DOI:10.1007/978-3-031-69491-2_18

PLoS Biol. 2024 Nov 19;22(11):e3002909. doi: 10.1371/journal.pbio.3002909. eCollection 2024 Nov.

ABSTRACT

Understanding the sequence and timing of brain functional network development at the beginning of human life is critically important from both normative and clinical perspectives. Yet, we presently lack rigorous examination of the longitudinal emergence of human brain functional networks over the birth transition. Leveraging a large, longitudinal perinatal functional magnetic resonance imaging (fMRI) data set, this study models developmental trajectories of brain functional networks spanning 25 to 55 weeks of post-conceptual gestational age (GA). The final sample includes 126 fetal scans (GA = 31.36 ± 3.83 weeks) and 58 infant scans (GA = 48.17 ± 3.73 weeks) from 140 unique subjects. In this study, we document the developmental changes of resting-state functional connectivity (RSFC) over the birth transition, evident at both network and graph levels. We observe that growth patterns are regionally specific, with some areas showing minimal RSFC changes, while others exhibit a dramatic increase at birth. Examples with birth-triggered dramatic change include RSFC within the subcortical network, within the superior frontal network, within the occipital-cerebellum joint network, as well as the cross-hemisphere RSFC between the bilateral sensorimotor networks and between the bilateral temporal network. Our graph analysis further emphasized the subcortical network as the only region of the brain exhibiting a significant increase in local efficiency around birth, while a concomitant gradual increase was found in global efficiency in sensorimotor and parietal-frontal regions throughout the fetal to neonatal period. This work unveils fundamental aspects of early brain development and lays the foundation for future work on the influence of environmental factors on this process.

PMID:39561110 | PMC:PMC11575827 | DOI:10.1371/journal.pbio.3002909

Hum Brain Mapp. 2024 Dec 1;45(17):e70075. doi: 10.1002/hbm.70075.

ABSTRACT

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of PTE, current methods for predicting its occurrence remain limited. In this study, we aimed to identify imaging-based markers for the prediction of PTE using machine learning. Specifically, we examined three imaging features: Lesion volumes, resting-state fMRI-based measures of functional connectivity, and amplitude of low-frequency fluctuation (ALFF). We employed three machine-learning methods, namely, kernel support vector machine (KSVM), random forest, and an artificial neural network (NN), to develop predictive models. Our results showed that the KSVM classifier, with all three feature types as input, achieved the best prediction accuracy of 0.78 AUC (area under the receiver operating characteristic (ROC) curve) using nested cross-validation. Furthermore, we performed voxel-wise and lobe-wise group difference analyses to investigate the specific brain regions and features that the model found to be most helpful in distinguishing PTE from non-PTE populations. Our statistical analysis uncovered significant differences in bilateral temporal lobes and cerebellum between PTE and non-PTE groups. Overall, our findings demonstrate the complementary prognostic value of MR-based markers in PTE prediction and provide new insights into the underlying structural and functional alterations associated with PTE.

PMID:39560185 | PMC:PMC11574740 | DOI:10.1002/hbm.70075

Hum Brain Mapp. 2024 Dec 1;45(17):e70077. doi: 10.1002/hbm.70077.

ABSTRACT

One of the fundamental questions in real-time functional magnetic resonance imaging neurofeedback (rt-fMRI NF) investigations is the definition of a suitable neural target for training. Previously, we applied a meta-analytical approach to define a network-level target for connectivity-based rt-fMRI NF in substance use disorders. The analysis yielded consistent connectivity alterations between the insula and anterior cingulate cortex (ACC) as well as the dorsal striatum and the ACC. In the current investigation, we addressed the feasibility of regulating this network and its functional relevance using connectivity-based neurofeedback. In a double-blind, sham-controlled design, 60 nicotine users were randomly assigned to the experimental or sham control group for one NF training session. The preregistered primary outcome was defined as improved inhibitory control performance after regulation of the target network compared to sham control. Secondary outcomes were (1) neurofeedback-specific changes in functional connectivity of the target network; (2) changes in smoking behavior and impulsivity measures; and (3) changes in resting-state connectivity profiles. Our results indicated no differences in behavioral measures after receiving feedback from the target network compared to the sham feedback. Target network connectivity was increased during regulation blocks compared to rest blocks, however, the experimental and sham groups could regulate to a similar degree. Accordingly, the observed activation patterns may be related to the mental strategies used during regulation attempts irrespective of the group assignment. We discuss several crucial factors regarding the efficacy of a single-session connectivity-based neurofeedback for the target network. This includes high fluctuation in the connectivity values of the target network that may impact controllability of the signal. To our knowledge, this investigation is the first randomized, double-blind controlled real-time fMRI study in nicotine users. This raises the question of whether previously observed effects in nicotine users are specific to the neurofeedback signal or reflect more general self-regulation attempts.

PMID:39559854 | PMC:PMC11574450 | DOI:10.1002/hbm.70077

Heliyon. 2024 Oct 23;10(21):e39735. doi: 10.1016/j.heliyon.2024.e39735. eCollection 2024 Nov 15.

ABSTRACT

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition.

PMID:39559231 | PMC:PMC11570486 | DOI:10.1016/j.heliyon.2024.e39735

Sci Rep. 2024 Nov 18;14(1):28421. doi: 10.1038/s41598-024-79527-4.

ABSTRACT

Pupil size and blink rates are heritable but the extent to which they interact with one another has not been properly investigated. Though changes in pupil size due to eye blinks have been reported, they are considered a pupillary artifact. In this study we used the HCP 7T fMRI dataset with resting state eye-tracking data obtained in monozygotic and dizygotic twins to assess their heritability and their interactions. For this purpose, we characterized the pupil dilation (positive peak) and constriction (negative peak) that followed blink events, which we describe as blink-induced pupillary response (BIPR). We show that the BIPR is highly consistent with a positive dilatory peak (D-peak) around 500ms and a negative constricting peak (C-peak) around 1s. These patterns were reproducible within- and between-subjects across two time points and differed by vigilance state (vigilant versus drowsy). By comparing BIPR between monozygotic and dizygotic twins we show that BIPR have a heritable component with significant additive genetic (A) and environmental (E) factors dominating the structural equation models, particularly in the time-domain for both D- and C-peaks (a2 between 42 and 49%) and shared effects (C) as observed in the amplitude domain for the C-peak. Blink duration, pupil size and blink rate were also found to be highly heritable (a2 up to 62% for pupil size). Our study provides evidence of that shared environmental and additive genetic factors influence BIPR and indicates that BIPR should not be treated as a coincidental artefact. Instead BIPR appears to be a component of a larger oculomotor system that we label here as Oculomotor Adaptive System, that is genetically determined.

PMID:39557891 | PMC:PMC11574171 | DOI:10.1038/s41598-024-79527-4

Cogn Neurodyn. 2024 Oct;18(5):2419-2432. doi: 10.1007/s11571-024-10095-z. Epub 2024 Mar 23.

ABSTRACT

Electroencephalography-based (EEG) microstate analysis is a promising and widely studied method in which spontaneous cerebral activity is segmented into sub second level quasi-stable states and analyzed. Currently it is being widely explored due to increasing evidence of the association of microstates with cognitive functioning and large-scale brain networks identified by functional magnetic resonance imaging (fMRI). In our study using the four archetypal microstates (A, B, C and D), we investigated the changes in resting state EEG microstate dynamics in persons with temporal lobe epilepsy (TLE) and idiopathic generalized epilepsy (IGE) compared to healthy controls (HC). Machine learning was applied to study its feasibility in differentiating between different groups using microstate statistics. We found significant differences in all parameters related to Microstate D (fronto-parietal network) in TLE patients and Microstate B (visual processing) in IGE patients compared to HCs. Occurrence, duration and time coverage of Microstate B was highest in IGE when compared to the other groups. We also found significant deviations in transition probabilities for both epilepsy groups, particularly into Microstate C (salience network) in IGE. Classification accuracy into clinical groups was found to exceed 70% using microstate parameters which improved on incorporating neuropsychological test differences. To the best of our knowledge, the current study is the first to compare and validate the use of microstate features to discriminate between two disparate epilepsy syndromes (TLE, IGE) and HCs using machine learning suggesting that resting state EEG microstates can be used for endophenotyping and to study resting state dysfunction in epilepsy.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-024-10095-z.

PMID:39555277 | PMC:PMC11564422 | DOI:10.1007/s11571-024-10095-z

Cogn Neurodyn. 2024 Aug;18(4):1651-1669. doi: 10.1007/s11571-023-10039-z. Epub 2023 Dec 1.

ABSTRACT

Dynamic functional connectivity (DFC) analysis using functional magnetic resonance imaging (fMRI) technology has attracted increasing attention in revealing brain dynamics in recent years. Although the nonnegative matrix factorization (NMF) method was applied to dynamic subgraph analysis to reveal brain dynamics, its application in DFC analysis was largely limited due to its nonnegative constraint on the input data. This study proposed the extended NMF (eNMF) method that allowed the input matrix and decomposed basis matrix to have negative values without altering the NMF algorithm. The eNMF method was applied to DFC analysis of both simulated and real resting fMRI data. The simulated data demonstrated that eNMF successfully decomposed the mixed-sign matrix into one positive matrix and one mixed-sign matrix. In contrast to K-means, eNMF extracted more accurate brain state patterns in all cases and estimated better DFC temporal properties for uneven brain state distribution. The real resting-fMRI data demonstrated that eNMF can provide more temporal measures of DFC and was more sensitive to detect intergroup differences of DFC than K-means. Results of eNMF revealed that the female group possibly showed worse relaxation and produced stronger spontaneous cognitive processes although they tended to spend more time in relaxation state and less time in states relevant to cognitive processes in contrast to the male group.

PMID:39554797 | PMC:PMC11564474 | DOI:10.1007/s11571-023-10039-z

Ther Adv Psychopharmacol. 2024 Nov 16;14:20451253241298871. doi: 10.1177/20451253241298871. eCollection 2024.

ABSTRACT

BACKGROUND: Emotional dysregulation, particularly unconscious catastrophic cognitions, plays a pivotal role in the genesis of panic disorder (PD). However, no studies have yet applied the percentage of amplitude fluctuation (PerAF) metric in resting-state functional magnetic resonance imaging to examine spontaneous neural functioning and its relation to catastrophic cognitions in PD.

OBJECTIVES: To explore the interplay between resting-state neural activity, functional connectivity (FC), and unconscious emotion regulation in individuals with PD.

DESIGN: Cross-sectional study.

METHODS: The study encompassed 51 participants, including 26 PD patients and 25 healthy individuals. The PerAF algorithm was employed to explore the local spontaneous neural activity in PD. Regions exhibiting aberrant spontaneous neural activity were used as seed points for whole-brain FC analysis. Correlations were utilized to examine associations between local neural activity patterns and neurocognitive assessments in PD.

RESULTS: The study revealed that compared to healthy individuals, PD patients exhibited elevated PerAF values in key emotion-regulation-related brain regions, including the ventromedial prefrontal cortex (vmPFC), striatum, amygdala, dorsomedial prefrontal cortex (dmPFC), and cerebellum. In addition, the resting-state FC between vmPFC and precuneus, as well as between the cerebellum and precuneus, was weakened in PD patients. Furthermore, positive associations were noted between PerAF measurements of vmPFC and amygdala and catastrophizing scores.

CONCLUSION: PD involves regional and network-level alterations in resting-state brain activity. The fronto-striatal-limbic circuits play a critical role in catastrophic-style emotion regulation in PD patients. Reduced FC within the default mode network and cerebellum-default mode network may signify a coordination anomaly in introspection and cognitive activities in PD. These findings complement the model of implicit emotion regulation in PD and suggest potential intervention targets.

PMID:39552918 | PMC:PMC11569504 | DOI:10.1177/20451253241298871

Psychol Med. 2024 Nov 18:1-12. doi: 10.1017/S0033291724001685. Online ahead of print.

ABSTRACT

BACKGROUND: Major psychiatric disorders (MPDs) are delineated by distinct clinical features. However, overlapping symptoms and transdiagnostic effectiveness of medications have challenged the traditional diagnostic categorisation. We investigate if there are shared and illness-specific disruptions in the regional functional efficiency (RFE) of the brain across these disorders.

METHODS: We included 364 participants (118 schizophrenia [SCZ], 80 bipolar disorder [BD], 91 major depressive disorder [MDD], and 75 healthy controls [HCs]). Resting-state fMRI was used to caclulate the RFE based on the static amplitude of low-frequency fluctuation, regional homogeneity, and degree centrality and corresponding dynamic measures indicating variability over time. We used principal component analysis to obtain static and dynamic RFE values. We conducted functional and genetic annotation and enrichment analysis based on abnormal RFE profiles.

RESULTS: SCZ showed higher static RFE in the cortico-striatal regions and excessive variability in the cortico-limbic regions. SCZ and MDD shared lower static RFE with higher dynamic RFE in sensorimotor regions than BD and HCs. We observed association between static RFE abnormalities with reward and sensorimotor functions and dynamic RFE abnormalities with sensorimotor functions. Differential spatial expression of genes related to glutamatergic synapse and calcium/cAMP signaling was more likely in the regions with aberrant RFE.

CONCLUSIONS: SCZ shares more regions with disrupted functional integrity, especially in sensorimotor regions, with MDD rather than BD. The neural patterns of these transdiagnostic changes appear to be potentially driven by gene expression variations relating to glutamatergic synapses and calcium/cAMP signaling. The aberrant sensorimotor, cortico-striatal, and cortico-limbic integrity may collectively underlie neurobiological mechanisms of MPDs.

PMID:39552391 | DOI:10.1017/S0033291724001685

Alcohol Clin Exp Res (Hoboken). 2024 Nov 17. doi: 10.1111/acer.15488. Online ahead of print.

ABSTRACT

A family history of alcohol use disorder (AUD) is associated with a significantly increased risk of developing AUD in one's lifetime. The previously reviewed literature suggests there are structural and functional neurobiological markers associated with familial AUD, but to our knowledge, no recent review has synthesized the latest findings across neuroimaging studies in this at-risk population. For this narrative review, we conducted keyword searches in electronic databases to find cross-sectional and longitudinal studies (2015-present) that used magnetic resonance imaging (MRI), diffusion tensor imaging, task-based functional MRI (fMRI), and/or resting state functional connectivity MRI. These studies were used to identify gray matter, white matter, and brain activity markers of risk and resilience in family history positive (FHP) individuals with a family history of AUD. FHP individuals have greater early adolescent thinning of executive functioning (frontal lobe) regions; however, some studies have reported null effects or greater gray matter volume and thickness relative to family history negative (FHN) peers without familial AUD. FHP individuals also have white matter microstructure alterations, such as reduced integrity of fronto-striatal pathways. Recent fMRI studies have found greater inhibitory control activity in FHP individuals, while reward-related findings are mixed. A growing interest in identifying intrinsic connectivity differences between FHP and FHN individuals has emerged in recent years. Familial AUD is related to both structural and functional brain alterations. Research should continue to focus on (1) longitudinal analyses with larger samples, (2) assessment of personal substance use and prenatal exposure to alcohol, (3) the effects of comorbid familial psychopathology, (4) examination of sex-specific markers of risk and resilience, (5) neural predictors of alcohol use initiation, and (6) brain-behavior relationships. These efforts would aid the design of neurobiologically informed prevention and intervention efforts focused on this at-risk population.

PMID:39552054 | DOI:10.1111/acer.15488

J Affect Disord. 2024 Nov 15:S0165-0327(24)01869-X. doi: 10.1016/j.jad.2024.11.036. Online ahead of print.

ABSTRACT

BACKGROUND: This study assessed adolescent brain-behavior relationships between large-scale dynamic functional network connectivity (FNC) and an integrated attention-deficit/hyperactivity disorder (ADHD) phenotype, including measures of inattention, impulsivity/hyperactivity and emotional dysregulation. Despite emotion dysregulation being a core clinical feature of ADHD, studies rarely assess its impact on large-scale FNC.

METHODS: We conducted resting-state functional magnetic resonance imaging in 78 adolescents (34 with ADHD) and obtained experimental and self-reported measures of inattention, impulsivity/hyperactivity, and emotional reactivity. We used multivariate analyses to evaluate group differences in dynamic FNC between the default mode, salience and central executive networks, meta-state functional connectivity and ADHD symptomology.

RESULTS: We present two significant group*behavior effects. Compared to controls, adolescents with ADHD had 1) diminished salience network-centered dynamic FNC that was driven by an integrated ADHD phenotype (p < .004, r = 0.57) and 2) more variable patterns of global connectivity, as measured through meta-state analysis, which were driven by heightened emotional reactivity (p < .002, r = 0.63).

CONCLUSIONS: Atypical patterns of dynamic FNC in adolescents with ADHD are associated with the affective and cognitive components of ADHD symptomology. Limitations include sample size and self-reported measures of emotional reactivity.

PMID:39551190 | DOI:10.1016/j.jad.2024.11.036

Neuroscience. 2024 Nov 15;563:239-251. doi: 10.1016/j.neuroscience.2024.11.030. Online ahead of print.

ABSTRACT

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

PMID:39550063 | DOI:10.1016/j.neuroscience.2024.11.030