Most recent paper

Heliyon. 2024 Mar 27;10(7):e28825. doi: 10.1016/j.heliyon.2024.e28825. eCollection 2024 Apr 15.

ABSTRACT

BACKGROUND: Altered neurodevelopment is a major clinical sequela of Preterm Birth (PTB) being currently unexplored in-utero.

AIMS: To study the link between fetal brain functional (FbF) connectivity and preterm birth, using resting-state functional magnetic resonance imaging (rs-fMRI).

STUDY DESIGN: Prospective single-centre cohort study.

SUBJECTS: A sample of 31 singleton pregnancies at 28-34 weeks assigned to a low PTB risk (LR) (n = 19) or high PTB risk (HR) (n = 12) group based on a) the Maternal Frailty Inventory (MaFra) for PTB risk; b) a case-specific PTB risk gradient.

METHODS: Fetal brain rs-fMRI was performed on 1.5T MRI scanner. First, directed causal relations representing fetal brain functional connectivity measurements were estimated using the Greedy Equivalence Search (GES) algorithm. HR vs. LR group differences were then tested with a novel ad-hoc developed Monte Carlo permutation test. Second, a MaFra-only random forest (RF) was compared against a MaFra-Neuro RF, trained by including also the most important fetal brain functional connections. Third, correlation and regression analyses were performed between MaFra-Neuro class probabilities and i) the GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks.

RESULTS: First, fewer fetal brain functional connections were evident in the HR group. Second, the MaFra-Neuro RF improved PTB risk prediction. Third, MaFra-Neuro class probabilities showed a significant association with: i) GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks.

CONCLUSION: Fetal brain functional connectivity is a novel promising predictor of PTB, linked to maternal risk profiles, ahead of birth, and clinical markers of neurodevelopmental risk, at birth, thus potentially "connecting" different PTB phenotypes.

PMID:38596101 | PMC:PMC11002256 | DOI:10.1016/j.heliyon.2024.e28825

Front Neurosci. 2024 Mar 26;18:1352409. doi: 10.3389/fnins.2024.1352409. eCollection 2024.

ABSTRACT

As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.

PMID:38595975 | PMC:PMC11002175 | DOI:10.3389/fnins.2024.1352409

Front Psychiatry. 2024 Mar 25;15:1337921. doi: 10.3389/fpsyt.2024.1337921. eCollection 2024.

ABSTRACT

The cerebellum has been consistently shown to be atypical in autism spectrum disorder (ASD). However, despite its known role in sensorimotor function, there is limited research on its association with sensory over-responsivity (SOR), a common and impairing feature of ASD. Thus, this study sought to examine functional connectivity of the sensorimotor cerebellum in ASD compared to typically developing (TD) youth and investigate whether cerebellar connectivity is associated with SOR. Resting-state functional connectivity of the sensorimotor cerebellum was examined in 54 ASD and 43 TD youth aged 8-18 years. Using a seed-based approach, connectivity of each sensorimotor cerebellar region (defined as lobules I-IV, V-VI and VIIIA&B) with the whole brain was examined in ASD compared to TD youth, and correlated with parent-reported SOR severity. Across all participants, the sensorimotor cerebellum was functionally connected with sensorimotor and visual regions, though the three seed regions showed distinct connectivity with limbic and higher-order sensory regions. ASD youth showed differences in connectivity including atypical connectivity within the cerebellum and increased connectivity with hippocampus and thalamus compared to TD youth. More severe SOR was associated with stronger connectivity with cortical regions involved in sensory and motor processes and weaker connectivity with cognitive and socio-emotional regions, particularly prefrontal cortex. These results suggest that atypical cerebellum function in ASD may play a role in sensory challenges in autism.

PMID:38590791 | PMC:PMC10999625 | DOI:10.3389/fpsyt.2024.1337921

Biol Psychiatry Cogn Neurosci Neuroimaging. 2024 Apr 6:S2451-9022(24)00084-3. doi: 10.1016/j.bpsc.2024.04.001. Online ahead of print.

ABSTRACT

BACKGROUND: Psilocybin is a widely studied psychedelic substance, which leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state's neurobiological and experiential patterns remains under-characterized as they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment.

METHODS: Healthy participants were randomized to receive either psilocybin (n=22) or placebo (n=27) and scanned for six minutes in eyes open resting state during the peak subjective drug effect (102 minutes post-treatment) in ultra-high field 7T MRI. The 5D-ASC Rating Scale was administered 360 minutes after drug intake.

RESULTS: Under psilocybin, there were alterations across all dimensions of the 5D-ASC scale, and widespread increases in averaged brain functional connectivity. Further time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low BOLD signal amplitude, suggesting heightened cortical arousal. In terms of neuro-experiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness, and secondly with visionary restructuralization.

CONCLUSIONS: Psilocybin generates profound alterations both at the brain and at the experiential level. We suggest that the brain's tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, providing insights into the neurophysiology and neuro-experiential qualities of the psychedelic state.

PMID:38588855 | DOI:10.1016/j.bpsc.2024.04.001

Ophthalmic Res. 2024 Apr 8. doi: 10.1159/000538380. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to explore the functional connectivity of the primary visual cortex (V1) in children with anisometropic amblyopia by using the resting-state functional connectivity (RSFC) analysis method and determine whether anisometropic amblyopia is associated with changes in brain function.

METHODS: Functional magnetic resonance imaging (fMRI) data were obtained from 16 children with anisometropia amblyopia (CAA group) and 12 healthy children (HC group) during the resting state. The Brodmann area 17 (BA17) was used as the region of interest (ROI), and the functional connection (FC) of V1 was analyzed in both groups. A two-sample t-test was used to analyze the FC value between the two groups. Pearson's correlation was used to analyze the correlation between the mean FC value in the brain function change area of the CAA group and the best corrected visual acuity (BCVA) of amblyopia. P<0.05 was considered statistically significant.

RESULTS: There were no significant differences in age and sex between the CAA and HC groups (p > 0.05). Compared to the HC group, the CAA group showed lower FC values in BA17 and the left medial frontal gyrus, as well as BA17 and the left triangle inferior frontal gyrus. Conversely, the CAA group showed higher FC values in BA17 and the left central posterior gyrus. Notably, BCVA in amblyopia did not correlate with the area of change in mean FC in the brain function of the CAA group.

CONCLUSION: Resting-state fMRI-based functional connectivity analysis indicates a significant alteration in V1 of children with anisometropic amblyopia. These findings contribute additional insights into the neuropathological mechanisms underlying visual impairment in anisometropic amblyopia.

PMID:38588644 | DOI:10.1159/000538380

Neuroimage Clin. 2024 Apr 3;42:103603. doi: 10.1016/j.nicl.2024.103603. Online ahead of print.

ABSTRACT

Antipsychotic drug treatment for schizophrenia (SZ) can alter brain structure and function, but it is unclear if specific regional changes are associated with treatment outcome. Therefore, we examined the effects of antipsychotic drug treatment on regional grey matter (GM) density, white matter (WM) density, and functional connectivity (FC) as well as associations between regional changes and treatment efficacy. SZ patients (n = 163) and health controls (HCs) (n = 131) were examined by structural magnetic resonance imaging (sMRI) at baseline, and a subset of SZ patients (n = 77) were re-examined after 8 weeks of second-generation antipsychotic treatment to assess changes in regional GM and WM density. In addition, 88 SZ patients and 81 HCs were examined by resting-state functional MRI (rs-fMRI) at baseline and the patients were re-examined post-treatment to examine FC changes. The Positive and Negative Syndrome Scale (PANSS) and MATRICS Consensus Cognitive Battery (MCCB) were applied to measure psychiatric symptoms and cognitive impairments in SZ. SZ patients were then stratified into response and non-response groups according to PANSS score change (≥50 % decrease or <50 % decrease, respectively). The GM density of the right cingulate gyrus, WM density of the right superior frontal gyrus (SFG) plus 5 other WM tracts were reduced in the response group compared to the non-response group. The FC values between the right anterior cingulate and paracingulate gyrus and left thalamus were reduced in the entire SZ group (n = 88) after treatment, while FC between the right inferior temporal gyrus (ITG) and right medial superior frontal gyrus (SFGmed) was increased in the response group. There were no significant changes in regional FC among the non-response group after treatment and no correlations with symptom or cognition test scores. These findings suggest that the right SFG is a critical target of antipsychotic drugs and that WM density and FC alterations within this region could be used as potential indicators in predicting the treatment outcome of antipsychotics of SZ.

PMID:38588618 | PMC:PMC11015154 | DOI:10.1016/j.nicl.2024.103603

IEEE/ACM Trans Comput Biol Bioinform. 2024 Apr 8;PP. doi: 10.1109/TCBB.2024.3386406. Online ahead of print.

ABSTRACT

The primary objective of imaging genetics research is to investigate the complex genotype-phenotype association for the disease under study. For example, to understand the impact of genetic variations over the brain functions and structure, the genotypic data such as single nucleotide polymorphism (SNP) is integrated with the phenotypic data such as imaging quantitative traits. The sparse models, based on canonical correlation analysis (CCA), are popular in this area to find the complex bi-multivariate genotype-phenotype association, as the number of features in genotypic and/or phenotypic data is significantly higher as compared to the number of samples. However, the sparse CCA based methods are, in general, unsupervised in nature, and fail to identify the diagnose-specific features those play an important role for the diagnosis and prognosis of the disease under study. In this regard, a new supervised model is proposed to study the complex genotype-phenotype association, by judiciously integrating the merits of CCA, linear discriminant analysis (LDA) and multi-task learning. The proposed model can identify the diagnose-specific as well as the diagnose-consistent features with significantly lower computational complexity. The performance of the proposed method, along with a comparison with the state-of-the-art methods, is evaluated on several synthetic data sets and one real imaging genetics data collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. In the current study, the SNP as genetic data and resting state functional MRI ( fMRI) as imaging data are integrated to find the complex genotype-phenotype association. An important finding is that the proposed method has better correlation value, improved noise resistance and stability, and also has better feature selection ability. All the results illustrate the power and capability of the proposed method to find the diagnostic group-specific imaging genetic association, which may help to understand the neurodegenerative disorder in a more comprehensive way.

PMID:38587960 | DOI:10.1109/TCBB.2024.3386406

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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

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

ABSTRACT

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 clinicaltrials.gov (NCT05912270).

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