Most recent paper

Mol Psychiatry. 2024 Oct 18. doi: 10.1038/s41380-024-02779-z. Online ahead of print.

ABSTRACT

Amyloid accumulation in Alzheimer's disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923/ALX001) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

PMID:39424929 | DOI:10.1038/s41380-024-02779-z

Sci Rep. 2024 Oct 18;14(1):24473. doi: 10.1038/s41598-024-74282-y.

ABSTRACT

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

PMID:39424632 | DOI:10.1038/s41598-024-74282-y

Neuroimage. 2024 Oct 16:120886. doi: 10.1016/j.neuroimage.2024.120886. Online ahead of print.

ABSTRACT

Brain imaging studies in complex regional pain syndrome (CRPS) have found mixed evidence for functional and structural changes in CRPS. In this cross-sectional study, we evaluated two patient cohorts from different centers and examined functional connectivity (rsFC) in 51 CRPS patients and 50 matched controls. rsFC was compared in predefined ROI pairs, but also in non-hypothesis driven analyses. Resting state (rs)fMRI changes in default mode network (DMN) and the degree rank order disruption index (kD) were additionally evaluated. Finally, imaging parameters were correlated with clinical severity and somatosensory function. Among predefined pairs, we found only weakly to moderately lower functional connectivity between the right nucleus accumbens and bilateral ventromedial prefrontal cortex in the infra-slow oscillations (ISO) band. The unconstrained ROI-to-ROI analysis revealed lower rsFC between the periaqueductal gray matter (PAG) and left anterior insula, and higher rsFC between the right sensorimotor thalamus and nucleus accumbens. In the correlation analysis, pain was positively associated with insulo-prefrontal rsFC, whereas sensorimotor thalamo-cortical rsFC was positively associated with tactile spatial resolution of the affected side. In contrast to previous reports, we found no group differences for kD or rsFC in the DMN, but detected overall lower data quality in patients. In summary, while some of the previous results were not replicated despite the larger sample size, novel findings from two independent cohorts point to potential down-regulated antinociceptive modulation by the PAG and increased connectivity within the reward system as pathophysiological mechanisms in CRPS. However, in light of the detected systematic differences in data quality between patients and healthy subjects, validity of rsFC abnormalities in CRPS should be carefully scrutinized in future replication studies.

PMID:39424016 | DOI:10.1016/j.neuroimage.2024.120886

Clin Transl Med. 2024 Oct;14(10):e70055. doi: 10.1002/ctm2.70055.

ABSTRACT

BACKGROUND: Huntington's disease (HD) is marked by irreversible loss of neuronal function for which currently no availability for disease-modifying treatment exists. Advances in the understanding of disease progression can aid biomarker development, which in turn can accelerate therapeutic discovery.

METHODS: We characterised the progression of altered dynamics of whole-brain network states in the zQ175DN mouse model of HD using a dynamic functional connectivity (FC) approach to resting-state fMRI and identified quasi-periodic patterns (QPPs) of brain activity constituting the most prominent resting-state networks.

RESULTS: The occurrence of the normative QPPs, as observed in healthy controls, was reduced in the HD model as the phenotype progressed. This uncovered progressive cessation of synchronous brain activity with phenotypic progression, which is not observed with the conventional static FC approaches. To better understand the potential underlying cause of the observed changes in these brain states, we further assessed how mutant huntingtin (mHTT) protein deposition affects astrocytes and pericytes - one of the most important effectors of neurovascular coupling, along phenotypic progression. Increased cell-type dependent mHTT deposition was observed at the age of onset of motor anomalies, in the caudate putamen, somatosensory and motor cortex, regions that are prominently involved in HD pathology as seen in humans.

CONCLUSION: Our findings provide meaningful insights into the development and progression of altered functional brain dynamics in this HD model and open new avenues in assessing the dynamics of whole brain states, through QPPs, in clinical HD research.

HIGHLIGHTS: Hyperactivity in the LCN-linked regions within short QPPs observed before motor impairment onset. DMLN QPP presents a progressive decrease in DMLN activity and occurrence along HD-like phenotype development. Breakdown of the LCN DMLN state flux at motor onset leads to a subsequent absence of the LCN DMLN QPP at an advanced HD-like stage.

PMID:39422700 | DOI:10.1002/ctm2.70055

Imaging Neurosci (Camb). 2024;2:10.1162/imag_a_00258. doi: 10.1162/imag_a_00258. Epub 2024 Aug 12.

ABSTRACT

The auditory system comprises multiple subcortical brain structures that process and refine incoming acoustic signals along the primary auditory pathway. Due to technical limitations of imaging small structures deep inside the brain, most of our knowledge of the subcortical auditory system is based on research in animal models using invasive methodologies. Advances in ultrahigh-field functional magnetic resonance imaging (fMRI) acquisition have enabled novel noninvasive investigations of the human auditory subcortex, including fundamental features of auditory representation such as tonotopy and periodotopy. However, functional connectivity across subcortical networks is still underexplored in humans, with ongoing development of related methods. Traditionally, functional connectivity is estimated from fMRI data with full correlation matrices. However, partial correlations reveal the relationship between two regions after removing the effects of all other regions, reflecting more direct connectivity. Partial correlation analysis is particularly promising in the ascending auditory system, where sensory information is passed in an obligatory manner, from nucleus to nucleus up the primary auditory pathway, providing redundant but also increasingly abstract representations of auditory stimuli. While most existing methods for learning conditional dependency structures based on partial correlations assume independently and identically Gaussian distributed data, fMRI data exhibit significant deviations from Gaussianity as well as high-temporal autocorrelation. In this paper, we developed an autoregressive matrix-Gaussian copula graphical model (ARMGCGM) approach to estimate the partial correlations and thereby infer the functional connectivity patterns within the auditory system while appropriately accounting for autocorrelations between successive fMRI scans. Our results show strong positive partial correlations between successive structures in the primary auditory pathway on each side (left and right), including between auditory midbrain and thalamus, and between primary and associative auditory cortex. These results are highly stable when splitting the data in halves according to the acquisition schemes and computing partial correlations separately for each half of the data, as well as across cross-validation folds. In contrast, full correlation-based analysis identified a rich network of interconnectivity that was not specific to adjacent nodes along the pathway. Overall, our results demonstrate that unique functional connectivity patterns along the auditory pathway are recoverable using novel connectivity approaches and that our connectivity methods are reliable across multiple acquisitions.

PMID:39421593 | PMC:PMC11485223 | DOI:10.1162/imag_a_00258

J Transl Med. 2024 Oct 17;22(1):945. doi: 10.1186/s12967-024-05757-9.

ABSTRACT

BACKGROUND: Although numerous studies have shown that bariatric surgery results in sustained weight loss and modifications in gut microbiota composition and cognitive function, the exact underlying mechanisms are unclear. This study aimed to investigate the effects of bariatric surgery on cognitive function through the microbiota-gut-brain axis (MGBA).

METHODS: Demographic data, serum samples, fecal samples, cognitive assessment scales, and resting-state functional connectivity magnetic resonance imaging (rs-fMRI) scans were obtained from 39 obese patients before and after (6 months) laparoscopic sleeve gastrectomy (LSG). PCA analysis, OPLS-DA analysis, and permutation tests were used to conduct fecal 16 S microbiota profiling, serum metabolomics, and neuroimaging analyses, and a bariatric surgery-specific rs-fMRI brain functional connectivity network was constructed. Spearman correlation analysis and Co-inertia analysis were employed to correlate significant alterations in cognitive assessment scales and resting-state functional connectivity difference networks with differential serum metabolites and 16 S microbiota data to identify key gut microbiota and serum metabolic factors.

RESULTS: LSG significantly reduced the weight of obese patients, with reductions of up to 28%. Furthermore, cognitive assessment scale measurements revealed that LSG enhanced cognitive functions, including memory (HVLT, p = 0.000) and executive function (SCWT, p = 0.008). Also, LSG significantly altered gut microbiota composition (p = 0.001), with increased microbial abundance and diversity (p < 0.05). Moreover, serum metabolite levels were significantly altered, revealing intergroup differences in 229 metabolites mapped to 72 metabolic pathways (p < 0.05, VIP > 1). Spearman correlation analysis among cognitive assessment scales, gut microbiota species, and serum metabolites revealed correlations with 68 gut microbiota species and 138 serum metabolites (p < 0.05). Furthermore, pairwise correlations were detected between gut microbiota and serum metabolites (p < 0.05). Functional neuroimaging analysis revealed that LSG increased functional connectivity in cognitive-related frontotemporal networks (FPN, p < 0.01). Additionally, normalization of the default mode network (DMN) and salience network (SN) connectivity was observed after LSG (p < 0.001). Further canonical correlation and correlation analysis suggested that the cognitive-related brain network changes induced by LSG were associated with key gut microbiota species (Akkermansia, Blautia, Collinsella, Phascolarctobacterium, and Ruminococcus, p < 0.05) and neuroactive metabolites (Glycine, L-Serine, DL-Dopa, SM (d18:1/24:1(15Z), p < 0.05).

CONCLUSION: These findings indicate the pathophysiological role of the microbiota-gut-brain axis in enhancing cognitive function after bariatric surgery, and the study provides a basis for clinical dietary adjustments, probiotic supplementation, and guidance for bariatric surgery, but further research is still needed.

TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2100049403. Registered 02 August 2021, https://www.chictr.org.cn/ .

PMID:39420319 | DOI:10.1186/s12967-024-05757-9

Neuroimage. 2024 Oct 15:120887. doi: 10.1016/j.neuroimage.2024.120887. Online ahead of print.

ABSTRACT

Current models of brain networks may potentially be improved by integrating our knowledge of structural connections, within and between circuits, with metrics of functional interactions between network nodes. The former may be obtained from diffusion MRI of white matter (WM), while the latter may be derived by measuring correlations between resting state BOLD signals from pairs of gray matter (GM) regions. From inspection of diffusion MRI data, it is clear that each WM voxel within a 3D image array may be traversed by multiple WM structural tracts, each of which connects a pair of GM nodes. We hypothesized that by appropriately weighting and then integrating the functional connectivity of each such connected pair, the overall engagement of any WM voxel in brain functions could be evaluated. This model introduces a structural constraint to earlier studies of WM engagement and addresses some limitations of previous efforts to relate structure and function. Using concepts derived from graph theory, we obtained spatial maps of WM engagement which highlight WM regions critical for efficient communications across the brain. The distributions of WM engagement are highly reproducible across subjects and depict a notable interdependence between the distribution of GM activities and the detailed organization of WM. Additionally, we provide evidence that the engagement varies over time and shows significant differences between genders. These findings suggest the potential of WM engagement as a measure of the integrity of normal brain functions and as a biomarker for neurological and cognitive disorders.

PMID:39419426 | DOI:10.1016/j.neuroimage.2024.120887

Brain Behav. 2024 Oct;14(10):e70106. doi: 10.1002/brb3.70106.

ABSTRACT

OBJECTIVE: Chronic kidney disease (CKD) is increasingly recognized as a risk factor for alterations in brain function. However, detecting early-stage symptoms and structural changes remains challenging, potentially leading to delayed treatment. In our study, we aimed to investigate spontaneous brain activity changes in CKD patients using resting-state functional magnetic resonance imaging (fMRI). Additionally, we explored the correlation between common biomarkers reflecting CKD severity and brain activity.

METHODS: We recruited a cohort of 22 non-dialysis-dependent CKD patients and 22 controls for resting-state fMRI scans. Amplitude of low-frequency fluctuations (ALFFs) and regional homogeneity (ReHo) were calculated to evaluate brain activity. Regression analysis was conducted to explore the correlations between biomarkers reflecting the severity of CKD and brain activity.

RESULTS: CKD patients exhibited reduced z-scored ALFF (zALFF) and mean ALFF (mALFF) in the bilateral putamen, right caudate nucleus, left anterior cingulate, and right precuneus. Changes in bilateral putamen were also found in smCohe-ReHo and szCohe-ReHo analyses. Urine albumin-to-creatinine ratio (UACR), urine protein-to-creatinine ratio (UPCR), and serum albumin levels were associated with attenuated putamen activity.

CONCLUSION: Non-dialysis-dependent CKD patients had changes in zALFF, mALFF, smCohe-ReHo, and szCohe-ReHo values in specific brain regions, especially bilateral putamen. UACR, UPCR, and serum albumin levels are associated with putamen activity attenuation in rs-fMRI.

PMID:39417474 | DOI:10.1002/brb3.70106

Ther Adv Ophthalmol. 2024 Oct 15;16:25158414241280201. doi: 10.1177/25158414241280201. eCollection 2024 Jan-Dec.

ABSTRACT

BACKGROUND: Charles Bonnet syndrome (CBS) is a condition in which people with vision loss experience complex visual hallucinations. These complex visual hallucinations may be caused by increased excitability in the visual cortex that are present in some people with vision loss but not others.

OBJECTIVES: We aimed to evaluate the association between γ-aminobutyric acid (GABA) in the visual cortex and CBS. We also tested the relationship among visually evoked responses, functional connectivity, and CBS.

DESIGN: This is a prospective, case-controlled, cross-sectional observational study.

METHODS: We applied 3-Tesla magnetic resonance spectroscopy, as well as task-based and resting state (RS) connectivity functional magnetic resonance imaging in six participants with CBS and six controls without CBS. GABA+ was measured in the early visual cortex (EVC) and in the lateral occipital cortex (LOC). Participants also completed visual acuity and cognitive tests, and the North-East Visual Hallucinations Interview.

RESULTS: The two groups were well-matched for age, gender, visual acuity and cognitive scores. There was no difference in GABA+ levels between groups in the visual cortex. Most participants showed the expected blood oxygenation level dependent (BOLD) activation to images of objects and the phase-scrambled control. Using a fixed effects analysis, we found that BOLD activation was greater in participants with CBS compared to controls. Analysis of RS connectivity with LOC and EVC showed little difference between groups. A fixed effects analysis showed a correlation between the extent of functional connectivity with LOC and hallucination strength.

CONCLUSION: Overall, our results provide no strong evidence for an association between GABAergic inhibition in the visual cortex and CBS. We only found subtle differences in visual function and connectivity between groups. These findings suggest that the neurochemistry and visual connectivity for people with Charles Bonnet hallucinations are comparable to a sight loss population. Differences between groups may emerge when investigating subtle and transient changes that occur at the time of visual hallucinations.

PMID:39416975 | PMC:PMC11481065 | DOI:10.1177/25158414241280201

Alzheimers Dement (Amst). 2024 Oct 16;16(4):e70013. doi: 10.1002/dad2.70013. eCollection 2024 Oct-Dec.

ABSTRACT

INTRODUCTION: Apathy is a significant feature in Alzheimer's disease (AD) and subjective cognitive impairment (SCI), though its mechanisms are not well established.

METHODS: An effort-based decision-making (EBDM) framework was applied to investigate apathy in 30 AD patients, 41 SCI participants, and 55 healthy controls (HC). Data were analyzed using a drift-diffusion model (DDM) to uncover latent psychological processes.

RESULTS: SCI participants reported higher apathy than AD patients and HC. However, informant reports of apathy in AD patients were higher than self-reports and indicated significant apathy compared to HC. Both the AD and SCI groups showed reduced sensitivity to effort changes, linked to executive dysfunction in AD and apathy in SCI. Increased resting functional cortical connectivity with the nucleus accumbens (NA) was associated with higher apathy in SCI.

DISCUSSION: These results highlight a similar disruption of EBDM in AD and SCI, differentially related to executive functioning in AD and apathy in SCI.

HIGHLIGHTS: This is the first study investigating apathy using an effort-based decision-making (EBDM) framework in Alzheimer's disease (AD) and subjective cognitive impairment (SCI).Self-reports underestimate apathy in AD patients when compared to informant reports and healthy controls (HC). SCI participants, in whom self and informant reports were more concordant, also showed higher degrees of apathy.Both AD and SCI groups showed reduced sensitivity to effort.Reduced sensitivity to effort correlates with executive dysfunction in AD and apathy, but not depression, in SCI.Increased nucleus accumbens (ventral striatum) connectivity with the frontoparietal network was associated with higher apathy scores in SCI.The results thus suggest that while AD and SCI can have similar deficits in EBDM, these deficits correlate with distinct clinical manifestations: executive dysfunction in AD and apathy in SCI.

PMID:39416486 | PMC:PMC11480904 | DOI:10.1002/dad2.70013

bioRxiv [Preprint]. 2024 Oct 7:2024.10.04.616707. doi: 10.1101/2024.10.04.616707.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity.

METHODS: We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients.

RESULTS: Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent internalizing-externalizing symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This depression-protective personality dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control.

CONCLUSIONS: Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.

PMID:39416217 | PMC:PMC11482755 | DOI:10.1101/2024.10.04.616707

bioRxiv [Preprint]. 2024 Oct 7:2024.10.05.615717. doi: 10.1101/2024.10.05.615717.

ABSTRACT

The brain's resting-state energy consumption is expected to be mainly driven by spontaneous activity. In our previous work, we extracted a wide range of features from resting-state fMRI (rs-fMRI), and used them to predict [18F]FDG PET SUVR as a proxy of glucose metabolism. Here, we expanded upon our previous effort by estimating [18F]FDG kinetic parameters according to Sokoloff's model, i.e., Ki (irreversible uptake rate), K1 (delivery), k3 (phosphorylation), in a large healthy control group. The parameters' spatial distribution was described at a high spatial resolution. We showed that while K1 is the least redundant, there are relevant differences between Ki and k3 (occipital cortices, cerebellum and thalamus). Using multilevel modeling, we investigated how much of the regional variability of [18F]FDG parameters could be explained by a combination of rs-fMRI variables only, or with the addition of cerebral blood flow (CBF) and metabolic rate of oxygen (CMRO2), estimated from 15O PET data. We found that combining rs-fMRI and CMRO2 led to satisfactory prediction of individual Ki variance (45%). Although more difficult to describe, Ki and k3 were both most sensitive to local rs-fMRI variables, while K1 was sensitive to CMRO2. This work represents the most comprehensive assessment to date of the complex functional and metabolic underpinnings of brain glucose consumption.

PMID:39416159 | PMC:PMC11482815 | DOI:10.1101/2024.10.05.615717

bioRxiv [Preprint]. 2024 Oct 12:2024.10.10.617666. doi: 10.1101/2024.10.10.617666.

ABSTRACT

Cerebrovascular reactivity (CVR) and cerebral pulsatility (CP) are important indicators of cerebrovascular health and have been shown to be associated with physical activity (PA). Sex differences have been shown to influence the impact of PA on cerebrovascular health. However, the sex-specific effects of PA on CP and CVR, particularly in relation to intensity and dosage of PA, remains unknown. Thus, this cross-sectional study aimed to evaluate the sex-specific effects of different intensities and doses of PA on CVR and CP. The Human Connectome - Aging dataset was used, including 626 participants (350 females, 276 males) aged 36-85 (mean age: 58.8 ± 14.1 years). Females were stratified into premenopausal and postmenopausal groups to assess the potential influence of menopausal status. Novel tools based solely on resting state fMRI data were used to estimate both CVR and CP. The International Physical Activity Questionnaire was used to quantify weekly self-reported PA as metabolic equivalent of task. Results indicated that both sexes and menopausal subgroups revealed negative linear relationships between relative CVR and PA. Furthermore, females presented a unique non-linear relationship between relative CVR and total PA in the cerebral cortex. In females, there were also relationships with total and walking PA in occipital and cingulate regions. In males, we observed relationships between total or vigorous PA and CVR in parietal and cingulate regions. Sex-specific effects were also observed with CP, whereby females benefited across a greater number of regions and intensities than males, especially in the postmenopause group. Overall, males and females appear to benefit from different amounts and intensities of PA, with menopause status significantly influencing the effect of PA on cerebrovascular outcomes, underscoring the need for sex-specific recommendations in promoting cerebrovascular health.

PMID:39416007 | PMC:PMC11482942 | DOI:10.1101/2024.10.10.617666

Brain Inj. 2024 Oct 16:1-12. doi: 10.1080/02699052.2024.2416545. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to compare psychosocial outcomes and functional neuroimaging among youth with concussion, youth with anxiety, and age- and sex-matched controls.

METHODS: Using archival data from the Adolescent Brain Cognitive DevelopmentSM Study, we analyzed between-group differences in psychosocial outcomes measured by the Child Behavior Checklist's internalizing and externalizing problem scales, and assessed brain function using resting-state fMRI network-region connectivity (specifically frontoparietal network (FPN) and default mode network (DMN) connectivity with the amygdala).

RESULTS: Significant differences in psychosocial outcomes were found across all groups, with the anxiety group reporting the most internalizing problems, followed by the concussion group which significantly differed from controls. Additionally, FPN-amygdala connectivity was significantly reduced in the concussion group only; this reduced connectivity did not predict psychosocial outcomes across groups.

CONCLUSION: This study provided preliminary findings that brain connectivity is reduced exclusively in individuals with concussion. Although disruptions were observed in the concussion group, further investigation is warranted to understand how disruptions may be associated with concussion symptoms. Studies that utilize well-defined control and study groups, and comprehensive cognitive and mental health measures will offer a deeper understanding of the relationship between brain function and psychosocial outcomes.

PMID:39415428 | DOI:10.1080/02699052.2024.2416545

Front Neuroimaging. 2024 Oct 1;3:1445952. doi: 10.3389/fnimg.2024.1445952. eCollection 2024.

ABSTRACT

Severe acute brain injury (SABI) with suppressed consciousness is a major societal burden, with early prognosis being crucial for life-and-death treatment decisions. Resting-state functional MRI (rs-fMRI) is promising for prognosis and identifying epileptogenic activity in SABI. While established for SABI prognosis and seizure networks (SzNET) identification in epilepsy, the rs-fMRI use for SzNET detection in SABI is limited. This study compared evolution of SzNET and resting-state networks (RSN) pre-to-post treatment in SABI and epilepsy, hypothesizing that changes would align with clinical evolution. Therapies included epilepsy surgery for the epilepsy group and antiseizure medication for the SABI group. Independent component analysis (ICA) was used to identify SzNET and RSNs in all rs-fMRI. High-frequency BOLD (HF-BOLD), an ICA power spectrum-based index, quantified RSN and SzNET changes by the patient. Confidence intervals measured HF-BOLD changes pre-to-post-therapy. Baseline HF-BOLD and HF-BOLD changes were compared using linear-mixed models and interaction tests. Five SABI and ten epilepsy patients were included. SzNET were identified in all SABI's pre-therapy rs-fMRI. The clinical changes in SABI and epilepsy were consistent with rs-fMRI findings across groups. HF-BOLD reduced in the epilepsy group RSN post-therapy (-0.78, 95% CI -3.42 to -0.33), but the evidence was insufficient to determine an HF-BOLD reduction in SABI patients or SzNET. The HF-BOLD change trend in pre-to-post epilepsy surgery scans paralleled the clinical improvement, suggesting that the power spectrum may quantify the degree of abnormality on ICA-derived networks. Despite limitations such as small sample sizes, this exploratory study provides valuable insights into network dysfunction in SABI and epilepsy.

PMID:39411721 | PMC:PMC11473429 | DOI:10.3389/fnimg.2024.1445952

J Clin Med. 2024 Sep 30;13(19):5856. doi: 10.3390/jcm13195856.

ABSTRACT

Introduction: Several studies have investigated the specific neural correlates of trans people, highlighting mixed results. This study aimed to compare the presence of specific functional connectivity and differences in cognitive profile and hormone levels in trans men diagnosed with gender dysphoria (GD), and a homogeneous group of cisgender men and cisgender women. Methods: A total of 42 participants (19 trans men, 11 cisgender men, and 12 cisgender women) underwent a resting state fMRI and were measured for blood levels of testosterone, estradiol, and progesterone. A neuropsychological battery evaluated executive functions, attention, visual-perceptual ability, verbal fluency, manual preference, and general intelligence. Results: Trans men showed weaker functional connectivity in the precentral gyrus, subcallosal cortex, paracingulate gyrus, temporal pole, and cingulate gyrus than cisgender men (p < 0.01). Trans men performed worse than cisgender men in verbal and visuospatial working memory but similarly to cisgender women (p < 0.05). In trans men, functional connectivity of the precentral gyrus correlated positively with testosterone (r = 0.459, p = 0.064) and negatively with estradiol (r = -0.654, p = 0.004) and progesterone blood levels (r = -0.475, p = 0.054). The cluster involving the subcallosal cortex showed a positive correlation with testosterone (r = 0.718, p = 0.001), and a negative correlation with estradiol (r = -0.602, p = 0.011). The functional connectivity from a cluster involving the paracingulate gyrus showed a positive correlation with testosterone (r = 0.592, p = 0.012). Conclusions: This study highlights the importance of overpassing the binary model by underlining the presence of neural pathways that could represent the peculiarity of the neural profile of people with GD.

PMID:39407916 | DOI:10.3390/jcm13195856

J Clin Med. 2024 Sep 25;13(19):5704. doi: 10.3390/jcm13195704.

ABSTRACT

Background: This review focuses on the prognostic role of resting-state functional magnetic resonance imaging (fMRI) in disorders of consciousness (DOCs). Several studies were conducted to determine the diagnostic accuracy in DOC patients to identify prognostic markers and to understand the neural correlates of consciousness. A correct diagnosis of consciousness in unresponsive or minimally responsive patients is important for prognostic and therapeutic management. Functional connectivity is considered as an important tool for the formulation of cerebral networks; it takes into account the primary sensorimotor, language, visual and central executive areas, where fMRI studies show damage in brain connectivity in the areas of frontoparietal networks in DOC patients. Methods: The integration of neuroimaging or neurophysiological methods could improve our knowledge of the neural correlates of clinical response after an acquired brain injury. The use of MRI is widely reported in the literature in different neurological diseases. In particular, fMRI is the most widely used brain-imaging technique to investigate the neural mechanisms underlying cognition and motor function. We carried out a detailed literature search following the relevant guidelines (PRISMA), where we collected data and results on patients with disorders of consciousness from the studies performed. Results: In this review, 12 studies were selected, which showed the importance of the prognostic role of fMRI for DOCs. Conclusions: Currently there are still few studies on this topic. Future studies using fMRI are to be considered an added value for the prognosis and management of DOCs.

PMID:39407763 | DOI:10.3390/jcm13195704

Commun Biol. 2024 Oct 15;7(1):1324. doi: 10.1038/s42003-024-07007-6.

ABSTRACT

Creativity is typically operationalized as divergent thinking (DT) ability, a form of higher-order cognition which relies on memory, attention, and other component processes. Despite recent advances, creativity neuroscience lacks a unified framework to model its complexity across neural, genetic, and cognitive scales. Using task-based fMRI from two independent samples and MVPA, we identified a neural pattern that predicts DT, validated through cognitive decoding, genetic data, and large-scale resting-state fMRI. Our findings reveal that DT neural patterns span brain regions associated with diverse cognitive functions, with positive weights in the default mode and frontoparietal control networks and negative weights in the visual network. The high correlation with the primary gradient of functional connectivity suggests that DT involves extensive integration from concrete sensory information to abstract, higher-level cognition, distinguishing it from other advanced cognitive functions. Moreover, neurobiological analyses show that the DT pattern is positively correlated with dopamine-related neurotransmitters and genes influencing neurotransmitter release, advancing the neurobiological understanding of creativity.

PMID:39402209 | DOI:10.1038/s42003-024-07007-6

Prog Neuropsychopharmacol Biol Psychiatry. 2024 Oct 12:111169. doi: 10.1016/j.pnpbp.2024.111169. Online ahead of print.

ABSTRACT

Levodopa (L-dopa) therapy is the most effective pharmacological treatment for motor symptoms of Parkinson's disease (PD). However, its effect on brain functional network dynamics is still unclear. Here, we recruited 26 PD patients and 24 healthy controls, and acquired their resting-state functional MRI data before (PD-OFF) and after (PD-ON) receiving 400 mg L-dopa. Using the independent component analysis and the sliding-window approach, we estimated the dynamic functional connectivity (dFC) and examined the effect of L-dopa on the temporal properties of dFC states, the variability of dFC and functional network topological organization. We found that PD-ON showed decreased mean dwell time in sparsely connected State 2 than PD-OFF, the transformation of the dFC state is more frequent and the variability of dFC was decreased within the auditory network and sensorimotor network in PD-ON. Our findings provide new insights to understand the dynamic neural activity induced by L-dopa therapy in PD patients.

PMID:39401562 | DOI:10.1016/j.pnpbp.2024.111169

Front Neuroimaging. 2024 Sep 27;3:1474060. doi: 10.3389/fnimg.2024.1474060. eCollection 2024.

ABSTRACT

OBJECTIVE: Spinal cord stimulation (SCS) is an invasive treatment option for patients suffering from chronic low-back pain (cLBP). It is an effective treatment that has been shown to reduce pain and increase the quality of life in patients. However, the activation of pain processing regions of cLBP patients receiving SCS has not been assessed using objective, quantitative functional imaging techniques. The purpose of the present study was to compare quantitative resting-state (rs)-fMRI and arterial spin labeling (ASL) measures between SCS patients and healthy controls and to correlate clinical measures with quantitative multimodal imaging indices in pain regions.

METHODS: Multi-delay 3D GRASE pseudo-continuous ASL and rs-fMRI data were acquired from five patients post-SCS with cLBP and five healthy controls. Three ASL measures and four rs-fMRI measures were derived and normalized into MNI space and smoothed. Averaged values for each measure from a pain atlas were extracted and compared between patients and controls. Clinical pain scores assessing intensity, sensitization, and catastrophizing, as well as others assessing global pain effects (sleep quality, disability, anxiety, and depression), were obtained in patients and correlated with pain regions using linear regression analysis.

RESULTS: Arterial transit time derived from ASL and several rs-fMRI measures were significantly different in patients in regions involved with sensation (primary somatosensory cortex and ventral posterolateral thalamus [VPL]), pain input (posterior short gyrus of the insula [PS]), cognition (dorsolateral prefrontal cortex [DLPC] and posterior cingulate cortex [PCC]), and fear/stress response (hippocampus and hypothalamus). Unidimensional pain rating and sensitization scores were linearly associated with PS, VPL, DLPC, PCC, and/or amygdala activity in cLBP patients.

CONCLUSION: The present results provide evidence that ASL and rs-fMRI can contrast functional activation in pain regions of cLBP patients receiving SCS and healthy subjects, and they can be associated with clinical pain evaluations as quantitative assessment tools.

PMID:39399386 | PMC:PMC11470492 | DOI:10.3389/fnimg.2024.1474060