Most recent paper

Spectral graph model for fMRI: a biophysical, connectivity-based generative model for the analysis of frequency-resolved resting state fMRI

Mon, 04/08/2024 - 18:00

bioRxiv [Preprint]. 2024 Mar 27:2024.03.22.586305. doi: 10.1101/2024.03.22.586305.


Resting state functional MRI (rs-fMRI) is a popular and widely used technique to explore the brain's functional organization and to examine if it is altered in neurological or mental disorders. The most common approach for its analysis targets the measurement of the synchronized fluctuations between brain regions, characterized as functional connectivity (FC), typically relying on pairwise correlations in activity across different brain regions. While hugely successful in exploring state- and disease-dependent network alterations, these statistical graph theory tools suffer from two key limitations. First, they discard useful information about the rich frequency content of the fMRI signal. The rich spectral information now achievable from advances in fast multiband acquisitions is consequently being under-utilized. Second, the analyzed FCs are phenomenological without a direct neurobiological underpinning in the underlying structures and processes in the brain. There does not currently exist a complete generative model framework for whole brain resting fMRI that is informed by its underlying biological basis in the structural connectome. Here, we propose that a different approach can solve both challenges at once: the use of an appropriately realistic yet parsimonious biophysical signal generation model followed by graph spectral (i.e. eigen) decomposition. We call this model a Spectral Graph Model (SGM) for fMRI, using which we can not only quantify the structure-function relationship in individual subjects, but also condense the variable and individual-specific repertoire of fMRI signal's spectral and spatial features into a small number of biophysically-interpretable parameters. We expect this model-based inference of rs-fMRI that seamlessly integrates with structure can be used to examine state and trait characteristics of structure-function relations in a variety of brain disorders.

PMID:38586057 | PMC:PMC10996488 | DOI:10.1101/2024.03.22.586305

In-vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth

Mon, 04/08/2024 - 18:00

bioRxiv [Preprint]. 2024 Mar 28:2023.06.22.546023. doi: 10.1101/2023.06.22.546023.


A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.

PMID:38586012 | PMC:PMC10996460 | DOI:10.1101/2023.06.22.546023

Parallel Multilink Group Joint ICA: Fusion of 3D Structural and 4D Functional Data Across Multiple Resting fMRI Networks

Mon, 04/08/2024 - 18:00

bioRxiv [Preprint]. 2024 Mar 27:2024.03.21.586091. doi: 10.1101/2024.03.21.586091.


Multimodal neuroimaging research plays a pivotal role in understanding the complexities of the human brain and its disorders. Independent component analysis (ICA) has emerged as a widely used and powerful tool for disentangling mixed independent sources, particularly in the analysis of functional magnetic resonance imaging (fMRI) data. This paper extends the use of ICA as a unifying framework for multimodal fusion, introducing a novel approach termed parallel multilink group joint ICA (pmg-jICA). The method allows for the fusion of gray matter maps from structural MRI (sMRI) data to multiple fMRI intrinsic networks, addressing the limitations of previous models. The effectiveness of pmg-jICA is demonstrated through its application to an Alzheimer's dataset, yielding linked structure-function outputs for 53 brain networks. Our approach leverages the complementary information from various imaging modalities, providing a unique perspective on brain alterations in Alzheimer's disease. The pmg-jICA identifies several components with significant differences between HC and AD groups including thalamus, caudate, putamen with in the subcortical (SC) domain, insula, parahippocampal gyrus within the cognitive control (CC) domain, and the lingual gyrus within the visual (VS) domain, providing localized insights into the links between AD and specific brain regions. In addition, because we link across multiple brain networks, we can also compute functional network connectivity (FNC) from spatial maps and subject loadings, providing a detailed exploration of the relationships between different brain regions and allowing us to visualize spatial patterns and loading parameters in sMRI along with intrinsic networks and FNC from the fMRI data. In essence, developed approach combines concepts from joint ICA and group ICA to provide a rich set of output characterizing data-driven links between covarying gray matter networks, and a (potentially large number of) resting fMRI networks allowing further study in the context of structure/function links. We demonstrate the utility of the approach by highlighting key structure/function disruptions in Alzheimer's individuals.

PMID:38585901 | PMC:PMC10996497 | DOI:10.1101/2024.03.21.586091

Whole-Brain Dynamics Disruptions in the Progression of Alzheimer's Disease: Understanding the Influence of Amyloid-Beta and Tau

Mon, 04/08/2024 - 18:00

bioRxiv [Preprint]. 2024 Mar 31:2024.03.29.587333. doi: 10.1101/2024.03.29.587333.


INTRODUCTION: Alzheimer's disease (AD) affects brain structure and function along its evolution, but brain network dynamic changes remain largely unknown.

METHODS: To understand how AD shapes brain activity, we investigated the spatiotemporal dynamics and resting state functional networks using the intrinsic ignition framework, which characterizes how an area transmits neuronal activity to others, resulting in different degrees of integration. Healthy participants, MCI, and AD patients were scanned using resting state fMRI. Mixed effects models were used to assess the impact of ABeta and tau, at the regional and whole-brain levels.

RESULTS: Dynamic complexity is progressively reduced, with Healthy participants showing higher metastability (i.e., a more complex dynamical regime over time) than observed in the other stages, while AD subjects showed the lowest.

DISCUSSION: Our study provides further insight into how AD modulates brain network dynamics along its evolution, progressively disrupting the whole-brain and resting state network dynamics.

PMID:38585882 | PMC:PMC10996678 | DOI:10.1101/2024.03.29.587333

Altered empathy processing in frontotemporal dementia A task-based fMRI study

Mon, 04/08/2024 - 18:00

bioRxiv [Preprint]. 2024 Mar 26:2024.03.21.586051. doi: 10.1101/2024.03.21.586051.


A lack of empathy, and particularly its affective components, is a core symptom of behavioural variant frontotemporal dementia (bvFTD). Visual exposure to images of a needle pricking a hand (pain condition) and Q-tips touching a hand (control condition) is an established functional magnetic resonance imaging (fMRI) paradigm used to investigate empathy for pain (EFP; pain condition minus control condition). EFP has been associated with increased blood oxygen level dependent (BOLD) signal in regions known to become atrophic in the early stages in bvFTD, including the anterior insula and the anterior cingulate. We therefore hypothesized that patients with bvFTD would display altered empathy processing in the EFP paradigm. Here we examined empathy processing using the EFP paradigm in 28 patients with bvFTD and 28 sex and age matched controls. Participants underwent structural MRI, task-based and resting-state fMRI. The Interpersonal Reactivity Index (IRI) was used as a measure of different facets of empathic function outside the scanner. The EFP paradigm was analysed at a whole brain level and using two regions-of-interest approaches, one based on a metanalysis of affective perceptual empathy versus cognitive evaluative empathy and one based on the controĺs activation pattern. In controls, EFP was linked to an expected increase of BOLD signal that displayed an overlap with the pattern of atrophy in the bvFTD patients (insula and anterior cingulate). Additional regions with increased signal were the supramarginal gyrus and the occipital cortex. These latter regions were the only ones that displayed increased BOLD signal in bvFTD patients. BOLD signal increase under the affective perceptual empathy but not the cognitive evaluative empathy region of interest was significantly greater in controls than in bvFTD patients. The controĺs rating on their empathic concern subscale of the IRI was significantly correlated with the BOLD signal in the EFP paradigm, as were an informantś ratings of the patientś empathic concern subscale. This correlation was not observed on other subscales of the IRI or when using the patient's self-ratings. Finally, controls and patients showed different connectivity patterns in empathy related networks during resting-state fMRI, mainly in nodes overlapping the ventral attention network. Our results indicate that reduced neural activity in regions typically affected by pathology in bvFTD is associated with reduced empathy processing, and a predictor of patientś capacity to experience affective empathy.

PMID:38585830 | PMC:PMC10996471 | DOI:10.1101/2024.03.21.586051

Modulations of resting-static functional connectivity on insular by electroacupuncture in subjective tinnitus

Mon, 04/08/2024 - 18:00

Front Neurol. 2024 Mar 22;15:1373390. doi: 10.3389/fneur.2024.1373390. eCollection 2024.


OBJECTIVE: To explore the modulations of electroacupuncture in subjective tinnitus (ST) by comparing the difference of functional connectivity (FC) in ST patients and healthy volunteers between the insular (INS) and the whole brain region.

METHODS: A total of 34 ST patients were selected into electroacupuncture group (EG) and 34 age- and sex-matched normal subjects were recruited into control group (CG). The EG received acupuncture at SI19 (Tinggong), GB11 (Touqiaoyin), TE17 (Yifeng), GV20 (Baihui), GV15 (Yamen), GV14 (Dazhui), SJ13 (Zhongzhu), among which the points of SI19 and GB11 were connected to the electroacupuncture instrument with the density wave of 2/50 Hz, and 3 treatments per week for 10 sessions in total. The severity of tinnitus was evaluated by Tinnitus Handicap Inventory (THI), the hearing status was recorded using pure tone audiometry, and resting-state functional magnetic resonance imaging (rs-fMRI) was performed on the brain before and after treatment, the CG received no intervention yet only rs-fMRI data were collected.

RESULTS: With the electroacupuncture treatment, the total THI score, average air conduction threshold of patients of EG were significantly lower than before (p < 0.01), and the total effective rate was 88.24%. Compared with CG, FC of ST patients between INS and left superior temporal gyrus and right hippocampal significantly decreased before treatment, while FC of ST patients between INS and right superior frontal gyrus, left middle frontal gyrus and right anterior cuneus significantly decreased after treatment (voxel p < 0.001, cluster p < 0.05, corrected with GRF). FC of ST patients between the INS and right middle frontal gyrus, left superior frontal gyrus and right paracentral lobule showed a significant decrease after treatment (voxel p < 0.001, cluster p < 0.05, corrected with GRF). In addition, THI score in EG was negatively correlated with the reduction of FC value in INS-left superior frontal gyrus before treatment (r = -0.41, p = 0.017). Therefore, this study suggests that abnormal FC of INS may be one of the significant central mechanisms of ST patients and can be modulated by electroacupuncture.

DISCUSSION: Electroacupuncture treatment can effectively reduce or eliminate tinnitus symptoms in ST patients and improve the hearing by decreasing FC between the INS and the frontal and temporal brain regions.

PMID:38585348 | PMC:PMC10995322 | DOI:10.3389/fneur.2024.1373390

Spontaneous brain activity in the hippocampal regions could characterize cognitive impairment in patients with Parkinson's disease

Mon, 04/08/2024 - 18:00

CNS Neurosci Ther. 2024 Apr;30(4):e14706. doi: 10.1111/cns.14706.


OBJECTIVE: This study aimed to investigate whether spontaneous brain activity can be used as a prospective indicator to identify cognitive impairment in patients with Parkinson's disease (PD).

METHODS: Resting-state functional magnetic resonance imaging (RS-fMRI) was performed on PD patients. The cognitive level of patients was assessed by the Montreal Cognitive Assessment (MoCA) scale. The fractional amplitude of low-frequency fluctuation (fALFF) was applied to measure the strength of spontaneous brain activity. Correlation analysis and between-group comparisons of fMRI data were conducted using Rest 1.8. By overlaying cognitively characterized brain regions and defining regions of interest (ROIs) based on their spatial distribution for subsequent cognitive stratification studies.

RESULTS: A total of 58 PD patients were enrolled in this study. They were divided into three groups: normal cognition (NC) group (27 patients, average MoCA was 27.96), mild cognitive impairment (MCI) group (21 patients, average MoCA was 23.52), and severe cognitive impairment (SCI) group (10 patients, average MoCA was 17.3). It is noteworthy to mention that those within the SCI group exhibited the most advanced chronological age, with an average of 74.4 years, whereas the MCI group displayed a higher prevalence of male participants at 85.7%. It was found hippocampal regions were a stable representative brain region of cognition according to the correlation analysis between the fALFF of the whole brain and cognition, and the comparison of fALFF between different cognitive groups. The parahippocampal gyrus was the only region with statistically significant differences in fALFF among the three cognitive groups, and it was also the only brain region to identify MCI from NC, with an AUC of 0.673. The paracentral lobule, postcentral gyrus was the region that identified SCI from NC, with an AUC of 0.941. The midbrain, hippocampus, and parahippocampa gyrus was the region that identified SCI from MCI, with an AUC of 0.926.

CONCLUSION: The parahippocampal gyrus was the potential brain region for recognizing cognitive impairment in PD, specifically for identifying MCI. Thus, the fALFF of parahippocampal gyrus is expected to contribute to future study as a multimodal fingerprint for early warning.

PMID:38584347 | PMC:PMC10999557 | DOI:10.1111/cns.14706

Analysis of changes in intrinsic neural timescales in male smoking addicts based on whole brain resting-state functional magnetic resonance imaging

Sun, 04/07/2024 - 18:00

Zhonghua Yi Xue Za Zhi. 2024 Apr 9;104(14):1168-1173. doi: 10.3760/cma.j.cn112137-20231010-00696.


Objective: To investigate the abnormal changes of intrinsic neural time scale (INT) in male smoking addicts based on whole brain resting state functional magnetic resonance imaging (rs-fMRI). Methods: A case-control study. The clinical data and whole brain rs-fMRI data of 139 male subjects, aged (34.1±8.8) years, recruited through the online platform from January 2019 to December 2021 were retrospectively analyzed. According to the existence of smoking addiction, they were divided into smoking addiction group (n=83) and healthy control group (n=56).INT was calculated to reflect the brain neural activity dynamics. Single sample t test was used to obtain the whole brain spatial distribution maps of INT in smoking addiction group and the control group. Then two-sample t test was conducted to explore the difference of INT between the smoking addition group and the healthy control group, with age and years of education as covariates. Finally, Spearman correlation analysis was used to explore the relationship between INT and nicotine dependence scale score and smoking index. Results: Subjects with smoking addiction and healthy control group showed a similar pattern of hierarchical neural timescales, namely shorter INT in sensorimotor areas and longer INT in parietal lobe, posterior cingulate cortex. In addition, in the smoking addiction group, the left medial occipital gyrus (peak t=-3.18), left suproccipital gyrus (peak t=-3.66), bilateral pericalar cleft cortex (left: peak t=-3.02, right: peak t=-3.22), bilateral lingual gyrus (left: peak t=-3.10, right: t peak=-3.04), left cuneus (peak t=-2.97), default network associated brain region [left anterior cuneus(peak t=-3.23), left angular gyrus (peak t=-3.07), and left posterior cingulate cortex (peak t=-3.54) were significantly lower than those of healthy controls (gaussian random field correction, voxel level all P<0.005, mass level all P<0.05). However, there was no significant correlation between INT and nicotine dependence scale score and smoking index (both P>0.05 after Bonferroni correction). Conclusion: Compared with healthy controls, smoking addicts showed abnormal changes in the dynamics of neural activity in the visual cortex and the default network.

PMID:38583048 | DOI:10.3760/cma.j.cn112137-20231010-00696

Cerebellar dysconnectivity in schizophrenia and bipolar disorder is associated with cognitive and clinical variables

Sat, 04/06/2024 - 18:00

Schizophr Res. 2024 Apr 6:S0920-9964(24)00139-7. doi: 10.1016/j.schres.2024.03.039. Online ahead of print.


BACKGROUND: Abnormal cerebellar functional connectivity (FC) has been implicated in the pathophysiology of schizophrenia (SCZ) and bipolar disorder (BD). However, the patterns of cerebellar dysconnectivity in these two disorders and their association with cognitive functioning and clinical symptoms have not been fully clarified. In this study, we examined cerebellar FC alterations in SCZ and BD-I and their association with cognition and psychotic symptoms.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 39 SCZ, 43 BD-I, and 61 healthy controls from the Consortium for Neuropsychiatric Phenomics dataset were examined. The cerebellum was parcellated into ten functional networks, and seed-based FC was calculated for each cerebellar system. Principal component analyses were used to reduce the dimensionality of the diagnosis-related FC and cognitive variables. Multiple regression analyses were used to assess the relationship between FC and cognitive and clinical data.

RESULTS: We observed decreased cerebellar FC with the frontal, temporal, occipital, and thalamic areas in individuals with SCZ, and a more widespread decrease in cerebellar FC in individuals with BD-I, involving the frontal, cingulate, parietal, temporal, occipital, and thalamic regions. SCZ had increased within-cerebellum and cerebellar frontal FC compared to BD-I. In BD-I, memory and verbal learning performances, which were higher compared to SCZ, showed a greater interaction with cerebellar FC patterns. Additionally, patterns of increased cortico-cerebellar FC were marginally associated with positive symptoms in patients.

CONCLUSIONS: Our findings suggest that shared and distinct patterns of cortico-cerebellar dysconnectivity in SCZ and BD-I could underlie cognitive impairments and psychotic symptoms in these disorders.

PMID:38582653 | DOI:10.1016/j.schres.2024.03.039

Connecting the dots: Motor and default mode network crossroads in post-stroke motor learning deficits

Fri, 04/05/2024 - 18:00

Neuroimage Clin. 2024 Apr 2;42:103601. doi: 10.1016/j.nicl.2024.103601. Online ahead of print.


BACKGROUND: Strokes frequently result in long-term motor deficits, imposing significant personal and economic burdens. However, our understanding of the underlying neural mechanisms governing motor learning in stroke survivors remains limited - a fact that poses significant challenges to the development and optimisation of therapeutic strategies.

OBJECTIVE: This study investigates the diversity in motor learning aptitude and its associated neurological mechanisms. We hypothesised that stroke patients exhibit compromised overall motor learning capacity, which is associated with altered activity and connectivity patterns in the motor- and default-mode-network in the brain.

METHODS: We assessed a cohort of 40 chronic-stage, mildly impaired stroke survivors and 39 age-matched healthy controls using functional Magnetic Resonance Imaging (fMRI) and connectivity analyses. We focused on neural activity and connectivity patterns during an unilateral motor sequence learning task performed with the unimpaired or non-dominant hand. Primary outcome measures included task-induced changes in neural activity and network connectivity.

RESULTS: Compared to controls, stroke patients showed significantly reduced motor learning capacity, associated with diminished cerebral lateralization. Task induced activity modulation was reduced in the motor network but increased in the default mode network. The modulated activation strength was associated with an opposing trend in task-induced functional connectivity, with increased connectivity in the motor network and decreased connectivity in the DMN.

CONCLUSIONS: Stroke patients demonstrate altered neural activity and connectivity patterns during motor learning with their unaffected hand, potentially contributing to globally impaired motor learning skills. The reduced ability to lateralize cerebral activation, along with the enhanced connectivity between the right and left motor cortices in these patients, may signify maladaptive neural processes that impede motor adaptation, possibly affecting long-term rehabilitation post-stroke. The contrasting pattern of activity modulation and connectivity alteration in the default mode network suggests a nuanced role of this network in post-stroke motor learning. These insights could have significant implications for the development of customised rehabilitation strategies for stroke patients.

PMID:38579595 | PMC:PMC11004993 | DOI:10.1016/j.nicl.2024.103601

Multi-hierarchy Network Configuration Can Predict Brain States and Performance

Fri, 04/05/2024 - 18:00

J Cogn Neurosci. 2024 Apr 4:1-20. doi: 10.1162/jocn_a_02153. Online ahead of print.


The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we proposed an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules, and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.

PMID:38579269 | DOI:10.1162/jocn_a_02153

Aberrant Brain Triple-Network Effective Connectivity Patterns in Type 2 Diabetes Mellitus

Fri, 04/05/2024 - 18:00

Diabetes Ther. 2024 Apr 5. doi: 10.1007/s13300-024-01565-y. Online ahead of print.


INTRODUCTION: Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type 2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines.

METHODS: In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected.

RESULTS: Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004).

CONCLUSION: These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

PMID:38578396 | DOI:10.1007/s13300-024-01565-y

Cognitive enrichment through art: a randomized controlled trial on the effect of music or visual arts group practice on cognitive and brain development of young children

Thu, 04/04/2024 - 18:00

BMC Complement Med Ther. 2024 Apr 4;24(1):141. doi: 10.1186/s12906-024-04433-1.


BACKGROUND: The optimal stimulation for brain development in the early academic years remains unclear. Current research suggests that musical training has a more profound impact on children's executive functions (EF) compared to other art forms. What is crucially lacking is a large-scale, long-term genuine randomized controlled trial (RCT) in cognitive neuroscience, comparing musical instrumental training (MIP) to another art form, and a control group (CG). This study aims to fill this gap by using machine learning to develop a multivariate model that tracks the interconnected brain and EF development during the academic years, with or without music or other art training.

METHODS: The study plans to enroll 150 children aged 6-8 years and randomly assign them to three groups: Orchestra in Class (OC), Visual Arts (VA), and a control group (CG). Anticipating a 30% attrition rate, each group aims to retain at least 35 participants. The research consists of three analytical stages: 1) baseline analysis correlating EF, brain data, age, gender, and socioeconomic status, 2) comparison between groups and over time of EF brain and behavioral development and their interactions, including hypothesis testing, and 3) exploratory analysis combining behavioral and brain data. The intervention includes intensive art classes once a week, and incremental home training over two years, with the CG receiving six annual cultural outings.

DISCUSSION: This study examines the potential benefits of intensive group arts education, especially contrasting music with visual arts, on EF development in children. It will investigate how artistic enrichment potentially influences the presumed typical transition from a more unified to a more multifaceted EF structure around age eight, comparing these findings against a minimally enriched active control group. This research could significantly influence the incorporation of intensive art interventions in standard curricula.

TRIAL REGISTRATION: The project was accepted after peer-review by the Swiss National Science Foundation (SNSF no. 100014_214977) on March 29, 2023. The study protocol received approval from the Cantonal Commission for Ethics in Human Research of Geneva (CCER, BASEC-ID 2023-01016), which is part of Swiss ethics, on October 25, 2023. The study is registered at (NCT05912270).

PMID:38575952 | PMC:PMC10993461 | DOI:10.1186/s12906-024-04433-1

Brain structures and functional connectivity in neglected children with no other types of maltreatment

Thu, 04/04/2024 - 18:00

Neuroimage. 2024 Apr 2;292:120589. doi: 10.1016/j.neuroimage.2024.120589. Online ahead of print.


Child maltreatment can adversely affect brain development, leading to vulnerabilities in brain structure and function and various psychiatric disorders. Among the various types of child maltreatment, neglect has the highest incidence rate (76.0%); however, data on its sole adverse influence on the brain remain limited. This case-control brain magnetic resonance imaging (MRI) study identified the changes in gray matter structure and function that distinguish neglected children with no other type of maltreatment (Neglect group, n = 23) from typically developing children (TD group, n = 140), and investigated the association between these structural and functional differences and specific psychosocial phenotypes observed in neglected children. Our results showed that the Neglect group had a larger right and left anterior cingulate cortex (R/L.ACC) and smaller left angular gyrus (L.AG) gray matter volume. The larger R/L.ACC was associated with hyperactivity and inattention. Resting-state functional analysis showed increased functional connectivity (FC) between the left supramarginal gyrus (L.SMG) in the salience network (SN) and the right middle frontal gyrus (R.MFG) simultaneously with a decrease in FC with the L.ACC for the same seed. The increased FC for the R.MFG was associated with difficulty in peer problems and depressive symptoms; a mediating effect was evident for depressive symptoms. These results suggest that the structural atypicality of the R/L.ACC indirectly contributes to the disturbed FCs within the SN, thereby exacerbating depressive symptoms in neglected children. In conclusion, exposure to neglect in childhood may lead to maladaptive brain development, particularly neural changes associated with depressive symptoms.

PMID:38575041 | DOI:10.1016/j.neuroimage.2024.120589

Deep Canonical Correlation Fusion Algorithm Based on Denoising Autoencoder for ASD Diagnosis and Pathogenic Brain Region Identification

Thu, 04/04/2024 - 18:00

Interdiscip Sci. 2024 Apr 4. doi: 10.1007/s12539-024-00625-y. Online ahead of print.


Autism Spectrum Disorder (ASD) is defined as a neurodevelopmental condition distinguished by unconventional neural activities. Early intervention is key to managing the progress of ASD, and current research primarily focuses on the use of structural magnetic resonance imaging (sMRI) or resting-state functional magnetic resonance imaging (rs-fMRI) for diagnosis. Moreover, the use of autoencoders for disease classification has not been sufficiently explored. In this study, we introduce a new framework based on autoencoder, the Deep Canonical Correlation Fusion algorithm based on Denoising Autoencoder (DCCF-DAE), which proves to be effective in handling high-dimensional data. This framework involves efficient feature extraction from different types of data with an advanced autoencoder, followed by the fusion of these features through the DCCF model. Then we utilize the fused features for disease classification. DCCF integrates functional and structural data to help accurately diagnose ASD and identify critical Regions of Interest (ROIs) in disease mechanisms. We compare the proposed framework with other methods by the Autism Brain Imaging Data Exchange (ABIDE) database and the results demonstrate its outstanding performance in ASD diagnosis. The superiority of DCCF-DAE highlights its potential as a crucial tool for early ASD diagnosis and monitoring.

PMID:38573456 | DOI:10.1007/s12539-024-00625-y

Emotional abuse mediated by negative automatic thoughts impacts functional connectivity during adolescence

Thu, 04/04/2024 - 18:00

Neurobiol Stress. 2024 Mar 19;30:100623. doi: 10.1016/j.ynstr.2024.100623. eCollection 2024 May.


BACKGROUND: Emotional abuse during childhood and adolescence is thought to be associated with the brain; however, the neural mechanism underlying the cognitive process remains unknown. Therefore, we aimed to investigate the mediating effect of negative automatic thoughts on the relationship between emotional abuse and resting-state functional connectivity (rsFC) during adolescence.

METHOD: Our community sample included 54 adolescents aged 13-17 years in the statistical analysis. Resting-state functional and structural magnetic resonance imaging (MRI) was performed, while emotional abuse and negative automatic thoughts were assessed using self-reported scales. A mediation analysis was used to assess the contributions of early traumatic events and negative automatic thoughts to resting functional connectivity.

RESULT: Higher negative automatic thoughts were associated with lower connectivity in the context of greater emotional abuse (i.e., suppression effect). Thus, the relationships between emotional abuse and connectivity in the precuneus (pCun)-medial prefrontal cortex, parahippocampal cortex-extrastriate cortex, and temporal cortex-temporal pole were decreased by negative automatic thoughts. In contrast, functional connections in the pCun-pCun, pCun-precuneus/posterior cingulate cortex, and nucleus accumbens-somatomotor areas were strongly mediated when emotionally abused adolescents reported a high tendency for negative automatic thoughts.

CONCLUSION: Negative automatic thoughts strengthened the relationship between emotional abuse and rsFC. These findings highlight the underlying cognitive processing of the traumatic event-neural system, supporting the use of cognitive therapy for post-traumatic symptoms.

PMID:38572483 | PMC:PMC10987907 | DOI:10.1016/j.ynstr.2024.100623

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

Thu, 04/04/2024 - 18:00

Heliyon. 2024 Mar 24;10(7):e28559. doi: 10.1016/j.heliyon.2024.e28559. eCollection 2024 Apr 15.


BACKGROUND: At present, the diagnosis of post-traumatic stress disorder(PTSD) mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging(DTI)are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis.

OBJECTIVES: According to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls).

METHODS: We searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors.

RESULTS: All ML techniques have an diagnostic accuracy rate above 70%,and support vector machine(SVM) are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis.

CONCLUSION: In contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

PMID:38571633 | PMC:PMC10988057 | DOI:10.1016/j.heliyon.2024.e28559

Concurrent brain structural and functional alterations in the thalamus of adult survivors of childhood brain tumors: a multimodal MRI study

Wed, 04/03/2024 - 18:00

Brain Res Bull. 2024 Apr 2;211:110937. doi: 10.1016/j.brainresbull.2024.110937. Online ahead of print.


Adult survivors of childhood brain tumors often present with cognitive deficits that affect their quality of life. Studying brain structure and function in brain tumor survivors can help understand the underlying mechanisms of their cognitive deficits to improve long-term prognosis of these patients. This study analyzed voxel-based morphometry (VBM) derived from T1-weighted MRI and the amplitude of low-frequency fluctuation (ALFF) from resting-state functional magnetic resonance imaging (rs-fMRI) to examine the structural and functional alterations in 35 brain tumor survivors using 35 matching healthy individuals as controls. Compared with healthy controls, brain tumor survivors had decreased gray matter volumes (GMV) in the thalamus and increased GMV in the superior frontal gyrus. Functionally, brain tumor survivors had lower ALFF values in the inferior temporal gyrus and medial prefrontal area and higher ALFF values in the thalamus. Importantly, we found concurrent but negatively correlated structural and functional alterations in the thalamus based on observed significant differences in GMV and ALFF values. These findings on concurrent brain structural and functional alterations provide new insights towards a better understanding of the cognitive deficits in brain tumor survivors.

PMID:38570077 | DOI:10.1016/j.brainresbull.2024.110937

Self-esteem mediates the relationship between the parahippocampal gyrus and decisional procrastination at resting state

Wed, 04/03/2024 - 18:00

Front Neurosci. 2024 Mar 19;18:1341142. doi: 10.3389/fnins.2024.1341142. eCollection 2024.


When faced with a conflict or dilemma, we tend to postpone or even avoid making a decision. This phenomenon is known as decisional procrastination. Here, we investigated the neural correlates of this phenomenon, in particular the parahippocampal gyrus (PHG) that has previously been identified in procrastination studies. In this study, we applied an individual difference approach to evaluate participants' spontaneous neural activity in the PHG and their decisional procrastination levels, assessed outside the fMRI scanner. We discovered that the fractional amplitude of low-frequency fluctuations (fALFF) in the caudal PHG (cPHG) could predict participants' level of decisional procrastination, as measured by the avoidant decision-making style. Importantly, participants' self-esteem mediated the relationship between the cPHG and decisional procrastination, suggesting that individuals with higher levels of spontaneous activity in the cPHG are likely to have higher levels of self-esteem and thus be more likely to make decisions on time. In short, our study broadens the PHG's known role in procrastination by demonstrating its link with decisional procrastination and the mediating influence of self-esteem, underscoring the need for further exploration of this mediation mechanism.

PMID:38567283 | PMC:PMC10986735 | DOI:10.3389/fnins.2024.1341142