Most recent paper

Neuroscience. 2024 Nov 21:S0306-4522(24)00634-1. doi: 10.1016/j.neuroscience.2024.11.046. Online ahead of print.

ABSTRACT

Chronic insomnia (CI) is a common sleep disorder in middle-aged and elderly individuals. Long-term sleep deprivation can lead to physical, mental, and cognitive damage. Resting-state networks (RSNs) in the brain are closely linked to cognition and behavior. Therefore, we investigated changes in RSNs to explore behavioral and cognitive abnormalities in middle-aged and elderly CI patients. Resting state functional magnetic resonance imaging (rs-fMRI) and independent component analysis were used to study the intrinsic functional connectivity (FC) of the RSNs in 36 CI patients (20 CI with cognitive impairment (CI-I) patients and 16 CI without cognitive impairment (CI-N) patients) and 20 healthy controls (HC). Two-sample t-tests were used to compare RSNs differences between CI and HC groups and the RSNs differences between CI-I and CI-N groups. Partial correlation analysis was used to explore the relationship between the significant abnormal brain regions in RSN and clinical scales. Compared with HCs, CI patients showed significant differences in multiple RSNs, and FC values in two brain regions within RSNs were correlated with clinical scales. Furthermore, compared with CI-N group, CI-I group also showed significantly altered FC in multiple RSNs. Moreover, FC values in the right middle frontal gyrus within right frontal parietal network of CI-I patients were negatively correlated with the Mini-Mental State Examination scores. These results may explain hyperarousal, decreased attention and motor function impairments in CI patients. Furthermore, the aberrant alterations of RSNs in CI-I patients may play a crucial role in the onset and progression of cognitive impairment in CI patients.

PMID:39579856 | DOI:10.1016/j.neuroscience.2024.11.046

J Pain. 2024 Nov 20:104740. doi: 10.1016/j.jpain.2024.104740. Online ahead of print.

ABSTRACT

Chronic pain is a debilitating condition associated with brain alterations. However, the variability in neuroimaging results across modalities necessitates a comprehensive multi-modal meta-analysis for a cohesive understanding. This study aims to elucidate brain alterations in chronic pain patients using a multi-modal meta-analysis approach encompassing structural, resting-state functional connectivity, and pain processing paradigms in functional magnetic resonance imaging. A systematic literature search was conducted across PubMed, OVID Embase, OVID Medline, and Web of Science, encompassing studies published up to May 30th, 2022, to identify relevant research articles on chronic pain and MRI techniques in three modalities. Inclusion criteria encompassed experiments reporting three modality brain alterations in chronic pain patients, with sufficient statistical thresholds and enough sample size. We conducted voxel-wise meta-analyses using seed-based d mapping to identify significant alterations in each modality. Additionally, conjunction analyses were executed to identify common alterations across these modalities. Ultimately, 47 structure studies, 37 resting state functional connectivity studies, and 41 pain-processing studies were selected for formal analysis. Chronic pain patients displayed notable structural and functional alterations in the insular cortex, characterized by reduced gray matter, disruptions in functional connectivity with the frontoparietal network, and enhanced activation during painful stimuli processing. Distinct activation patterns were observed in the left and right insular cortex for pain stimulus processing versus anticipation. Furthermore, the superior temporal gyrus and superior frontal gyrus exhibited joint alterations across modalities. This multi-modal meta-analysis reveals consistent brain alterations in chronic pain patients, shedding light on the complex interplay between structural and functional changes. PERSPECTIVE: This multi-modal meta-analysis integrates findings from structural, resting-state functional connectivity, and pain processing paradigms in fMRI, revealing consistent brain alterations in chronic pain patients. Notable brain changes highlight the intricate interplay between structural and functional brain changes, advancing our understanding of chronic pain's neural underpinnings.

PMID:39577824 | DOI:10.1016/j.jpain.2024.104740

Neuroscience. 2024 Nov 20:S0306-4522(24)00606-7. doi: 10.1016/j.neuroscience.2024.11.024. Online ahead of print.

ABSTRACT

Patients with Bipolar Disorder type I (BD-I) exhibit maladaptive risky decision-making, which is related to impulsivity, suicide attempts, and aggressive behavior. Currently, there is a lack of effective predictive methods for early intervention in risky behaviors for patients with BD-I. This study aimed to predict risky behavior in patients with BD-I using resting-state functional magnetic resonance imaging (rs-fMRI). We included 48 patients with BD-I and 124 healthy controls (HC) and constructed voxel-wise functional connectivity (FC), dynamic FC (dFC), effective connectivity (EC), and dynamic EC (dEC) for each subject. The Balloon Analogue Risk Task (BART) was employed to measure the risky decision-making of all participants. We applied connectome-based predictive modeling (CPM) with five regression algorithms to predict risky behaviors as well as Barratt Impulsivity Scale (BIS) scores. Results showed that the BD-I had significantly lower risky adjusted pump scores compared to HC. The dEC-based linear regression-CPM model exhibited significant predictive ability for the adjusted pump scores in BD-I, while no significant predictive power was observed in HC. Furthermore, this model successfully predicted non-planning impulsiveness, motor impulsiveness, and BIS total score, but failed for attentional impulsiveness in BD-I. These findings provide a foundation for future work in predicting risky behaviors of psychiatric patients by using voxel-wise dEC underlying resting state.

PMID:39577688 | DOI:10.1016/j.neuroscience.2024.11.024

Hum Brain Mapp. 2024 Dec 1;45(17):e70064. doi: 10.1002/hbm.70064.

ABSTRACT

Human behavior can be remarkably shaped by experience, such as the removal of sensory input. Many studies of conditions such as stroke, limb amputation, and vision loss have examined how removal of input changes brain function. However, an important question yet to be answered is: when input is lost, does the brain change its connectivity to preferentially use some remaining inputs over others? In individuals with healthy vision, the central portion of the retina is preferentially used for everyday visual tasks, due to its ability to discriminate fine details. When central vision is lost in conditions like macular degeneration, peripheral vision must be relied upon for those everyday tasks, with some portions receiving "preferential" usage over others. Using resting-state fMRI collected during total darkness, we examined how deprivation and preferential usage influence the intrinsic functional connectivity of sensory cortex by studying individuals with selective vision loss due to late stages of macular degeneration. Specifically, we examined functional connectivity between category-selective visual areas and the cortical representation of three areas of the retina: the lesioned area, a preferentially used region of the intact retina, and a non-preferentially used region. We found that cortical regions representing spared portions of the peripheral retina, regardless of whether they are preferentially used, exhibit plasticity of intrinsic functional connectivity in macular degeneration. Cortical representations of spared peripheral retinal locations showed stronger connectivity to MT, a region involved in processing motion. These results suggest that the long-term loss of central vision can produce widespread effects throughout spared representations in early visual cortex, regardless of whether those representations are preferentially used. These findings support the idea that connections to visual cortex maintain the capacity for change well after critical periods of visual development.

PMID:39575904 | PMC:PMC11583081 | DOI:10.1002/hbm.70064

Soc Cogn Affect Neurosci. 2024 Nov 22:nsae077. doi: 10.1093/scan/nsae077. Online ahead of print.

ABSTRACT

Emotion regulation flexibility (ERF) refers to one's ability to respond flexibly in complex environments. Adaptiveness of ERF has been associated with cognitive flexibility, which can be improved by task-switching training. However, the impact of task-switching training on ERF and its underlying neural mechanisms remains unclear. To address this issue, we examined the effects of training on individuals' adaptiveness of ERF by assessing altered brain network patterns. Two groups of participants completed behavioral experiments and resting-state fMRI before and after training. Behavioral results showed higher adaptiveness scores and network analysis observed a higher number of connectivity edges, in the training group compared to the control group. Moreover, we found decreased connectivity strength within the default mode network (DMN) and increased connectivity strength within the frontoparietal network (FPN) in the training group. Furthermore, the task-switch training also led to decreased DMN-FPN interconnectivity, which was significantly correlated to increased adaptiveness of ERF scores. These findings suggest that the adaptiveness of ERF can be supported by altered patterns with the brain network through task-switch training, especially the increased network segregation between the DMN and FPN.

PMID:39575823 | DOI:10.1093/scan/nsae077

Imaging Neurosci (Camb). 2024 Nov 12;2:1-52. doi: 10.1162/imag_a_00347. eCollection 2024 Nov 1.

ABSTRACT

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc.py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc.py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but also first outputs a fully commented processing script that the users can read, query, interpret, and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc.py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc.py here using a set of task-based and resting-state FMRI example commands.

PMID:39575179 | PMC:PMC11576932 | DOI:10.1162/imag_a_00347

ArXiv [Preprint]. 2024 Oct 30:arXiv:2410.23515v1.

ABSTRACT

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, especially for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.

PMID:39575120 | PMC:PMC11581107

medRxiv [Preprint]. 2024 Oct 29:2024.10.27.24316243. doi: 10.1101/2024.10.27.24316243.

ABSTRACT

Alzheimer's disease (AD) is characterized by a long preclinical stage during which molecular markers of amyloid beta and tau pathology rise, but there is minimal neurodegeneration or cognitive decline. Previous literature suggests that measures of brain function might be more sensitive to neuropathologic burden during the preclinical stage of AD than conventional measures of macrostructure, such as cortical thickness. However, among studies that used resting-state functional Magnetic Resonance Imaging (fMRI) acquisitions with Blood Oxygenation Level Dependent (BOLD) contrast, most employed connectivity-based analytic approaches, which discard information about the amplitude of spontaneous brain activity. Consequently, little is known about the effects of amyloid and tau pathology on BOLD amplitude. To address this knowledge gap, we characterized the effects of preclinical and prodromal AD on the amplitude of low-frequency fluctuations (ALFF) of the BOLD signal both at the whole-brain level and, at a more granular level, focused on subregions of the medial temporal lobe. We observed reduced ALFF in both preclinical and prodromal AD. In preclinical AD, amyloid positivity was associated with a spatially diffuse ALFF reduction in the frontal, medial parietal, and lateral temporal association cortices, while tau pathology was negatively associated with ALFF in the entorhinal cortex. These ALFF effects were observed in the absence of observable macrostructural changes in preclinical AD and remained after adjusting for structural atrophy in prodromal AD, indicating that ALFF offers additional sensitivity to early disease processes beyond what is provided by traditional structural imaging biomarkers of neurodegeneration. We conclude that ALFF may be a promising imaging-based biomarker for assessing the effects of amyloid-clearing immunotherapies in preclinical AD.

PMID:39574853 | PMC:PMC11581098 | DOI:10.1101/2024.10.27.24316243

Front Neurol. 2024 Nov 6;15:1490763. doi: 10.3389/fneur.2024.1490763. eCollection 2024.

ABSTRACT

Resting-state functional MRI (fMRI) has revealed functional changes at the cortical level in degenerative cervical myelopathy (DCM) patients. The aim of this study was to systematically integrate static and dynamic functional connectivity (FC) to unveil abnormalities of functional networks of DCM patients and to analyze the prognostic value of these abnormalities for patients using resting-state fMRI. In this study, we collected clinical data and fMRI data from 44 DCM patients and 39 healthy controls (HC). Independent component analysis (ICA) was performed to investigate the group differences of intra-network FC. Subsequently, both static and dynamic FC were calculated to investigate the inter-network FC alterations in DCM patients. k-means clustering was conducted to assess temporal properties for comparison between groups. Finally, the support vector machine (SVM) approach was performed to predict the prognosis of DCM patients based on static FC, dynamic FC, and fusion of these two metrics. Relative to HC, DCM patients exhibited lower intra-network FC and higher inter-network FC. DCM patients spent more time than HC in the state in which both patients and HC were characterized by strong inter-network FC. Both static and dynamic FC could successfully classify DCM patients with different surgical outcomes. The classification accuracy further improved after fusing the dynamic and static FC for model training. In conclusion, our findings provide valuable insights into the brain mechanisms underlying DCM neuropathology on the network level.

PMID:39574511 | PMC:PMC11580013 | DOI:10.3389/fneur.2024.1490763

Front Neural Circuits. 2024 Nov 6;18:1497897. doi: 10.3389/fncir.2024.1497897. eCollection 2024.

ABSTRACT

Local and global functional connectivity densities (lFCD and gFCD, respectively), derived from functional magnetic resonance imaging (fMRI) data, represent the degree of functional centrality within local and global brain networks. While these methods are well-established for mapping brain connectivity, the molecular and synaptic foundations of these connectivity patterns remain unclear. Glutamate, the principal excitatory neurotransmitter in the brain, plays a key role in these processes. Among its receptors, the α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) is crucial for neurotransmission, particularly in cognitive functions such as learning and memory. This study aimed to examine the association of the AMPAR density and FCD metrics of intraregional and interregional functional centrality. Using [11C]K-2, a positron emission tomography (PET) tracer specific for AMPARs, we measured AMPAR density in the brains of 35 healthy participants. Our findings revealed a strong positive correlation between AMPAR density and both lFCD and gFCD-lFCD across the entire brain. This correlation was especially notable in key regions such as the anterior cingulate cortex, posterior cingulate cortex, pre-subgenual frontal cortex, Default Mode Network, and Visual Network. These results highlight that postsynaptic AMPARs significantly contribute to both local and global functional connectivity in the brain, particularly in network hub regions. This study provides valuable insights into the molecular and synaptic underpinnings of brain functional connectomes.

PMID:39568980 | PMC:PMC11576226 | DOI:10.3389/fncir.2024.1497897

BMC Psychiatry. 2024 Nov 20;24(1):835. doi: 10.1186/s12888-024-06283-0.

ABSTRACT

BACKGROUND: Psychotic depression (PD) and schizophrenia (SCZ) share overlapping symptoms yet differ in etiology, progression, and treatment approaches. Differentiating these disorders through symptom-based diagnosis is challenging, emphasizing the need for a clearer understanding of their distinct cognitive and neural mechanisms.

AIM: This study aims to compare cognitive impairments and brain functional activities in PD and SCZ to pinpoint distinguishing characteristics of each disorder.

METHODS: We evaluated cognitive function in 42 PD and 30 SCZ patients using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) values were derived from rs-fMRI data, and group differences in RBANS scores were analyzed. Additionally, Pearson correlation analysis was performed to assess the relationship between cognitive domains and brain functional metrics.

RESULTS: (1) The SCZ group showed significantly lower RBANS scores than the PD group across all cognitive domains, particularly in visuospatial/constructional ability and delayed memory (p < 0.05); (2) The SCZ group exhibited a significantly higher ReHo value in the left precuneus compared to the PD group (p < 0.05); (3) A negative correlation was observed between visuospatial construction, delayed memory scores, and the ReHo value of the left precuneus.

CONCLUSION: Cognitive impairment is more pronounced in SCZ than in PD, with marked deficits in visuospatial and memory domains. Enhanced left precuneus activity further differentiates SCZ from PD and correlates with cognitive impairments in both disorders, providing neuroimaging-based evidence to aid differential diagnosis and insights into cognitive dysfunction mechanisms, while also paving a clearer path for psychiatric research.

PMID:39567972 | PMC:PMC11577850 | DOI:10.1186/s12888-024-06283-0

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