New resting-state fMRI related studies at PubMed

Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data

Fri, 10/07/2022 - 10:00

Netw Neurosci. 2022 Jul 1;6(3):634-664. doi: 10.1162/netn_a_00247. eCollection 2022 Jul.

ABSTRACT

Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks. Additionally, we identify new edges generating additional paths. Moreover, although paths exist in both groups, these paths take unique trajectories and have a significant contribution to the decomposition. The proposed path analysis provides a way to characterize individuals by evaluating changes in paths, rather than just focusing on the pairwise relationships. Our results show promise for identifying path-based metrics in neuroimaging data.

PMID:36204419 | PMC:PMC9531579 | DOI:10.1162/netn_a_00247

Altered Brain Functional Connectivity in Female Athletes Over the Course of a Season of Collision or Contact Sports

Fri, 10/07/2022 - 10:00

Neurotrauma Rep. 2022 Sep 19;3(1):377-387. doi: 10.1089/neur.2022.0010. eCollection 2022.

ABSTRACT

University athletes are exposed to numerous impacts to the body and head, though the potential cumulative effects of such hits remain elusive. This study examined resting-state functional connectivity (rsFC) of brain networks in female varsity athletes over the course of a season. Nineteen female university athletes involved in collision (N = 12) and contact (N = 7) sports underwent functional magnetic resonance imaging scans at both pre- and post-season. A group-level independent component analysis (ICA) was used to investigate differences in rsFC over the course of a season and differences between contact and collision sport athletes. Decreased rsFC was observed over the course of the season between the default mode network (DMN) and regions in the frontal, parietal, and occipital lobe (p false discovery rate, ≤0.05) driven by differences in the contact group. There was also a main effect of group in the dorsal attention network (DAN) driven by differences between contact and collision groups at pre-season. Differences identified over the course of a season of play indicate largely decreased rsFC within the DMN, and level of contact was associated with differences in rsFC of the DAN. The association between exposure to repetitive head impacts (RHIs) and observed changes in network rsFC supplements the growing literature suggesting that even non-concussed athletes may be at risk for changes in brain functioning. However, the complexity of examining the direct effects of RHIs highlights the need to consider multiple factors, including mental health and sport-specific training and expertise, that may potentially be associated with neural changes.

PMID:36204391 | PMC:PMC9531888 | DOI:10.1089/neur.2022.0010

A Multimodal Multilevel Neuroimaging Model for Investigating Brain Connectome Development

Fri, 10/07/2022 - 10:00

J Am Stat Assoc. 2022;117(539):1134-1148. doi: 10.1080/01621459.2022.2055559. Epub 2022 Apr 25.

ABSTRACT

Recent advancements of multimodal neuroimaging such as functional MRI (fMRI) and diffusion MRI (dMRI) offers unprecedented opportunities to understand brain development. Most existing neurodevelopmental studies focus on using a single imaging modality to study microstructure or neural activations in localized brain regions. The developmental changes of brain network architecture in childhood and adolescence are not well understood. Our study made use of dMRI and resting-state fMRI imaging data sets from Philadelphia Neurodevelopmental Cohort (PNC) study to characterize developmental changes in both structural as well as functional brain connectomes. A multimodal multilevel model (MMM) is developed and implemented in PNC study to investigate brain maturation in both white matter structural connection and intrinsic functional connection. MMM addresses several major challenges in multimodal connectivity analysis. First, by using a first-level data generative model for observed measures and a second-level latent network modeling, MMM effectively infers underlying connection states from noisy imaging-based connectivity measurements. Secondly, MMM models the interplay between the structural and functional connections to capture the relationship between different brain connectomes. Thirdly, MMM incorporates covariate effects in the network modeling to investigate network heterogeneity across subpopoulations. Finally, by using a module-wise parameterization based on brain network topology, MMM is scalable to whole-brain connectomics. MMM analysis of the PNC study generates new insights in neurodevelopment during adolescence including revealing the majority of the white fiber connectivity growth are related to the cognitive networks where the most significant increase is found between the default mode and the executive control network with a 15% increase in the probability of structural connections. We also uncover functional connectome development mainly derived from global functional integration rather than direct anatomical connections. To the best of our knowledge, these findings have not been reported in the literature using multimodal connectomics. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

PMID:36204347 | PMC:PMC9531911 | DOI:10.1080/01621459.2022.2055559

EcoMeditation Modifies Brain Resting State Network Activity

Fri, 10/07/2022 - 10:00

Innov Clin Neurosci. 2022 Jul-Sep;19(7-9):61-70.

ABSTRACT

BACKGROUND: The tendency of the mind to wander, a characteristic of the brain's default mode network (DMN), correlates with increased unhappiness and self-referential processing and is a deterrent to establishing a consistent meditation practice. The objective of this study was to test the impact of a secular physiological method of meditation. We hypothesized that EcoMeditation would produce increases in neural communication in brain regions associated with compassion and prosociality and decreases in self-referencing networks, such as the DMN, and that these changes would be found in the experimental group, but not the control group.

METHODS: Participants (n=38) were randomized into two groups, and the final sample consisted of 25 participants. One group listened daily to a 22-minute EcoMeditation audio track, while the other used an active control. Functional magnetic resonance imaging (fMRI) was used to assess brain function before and after four weeks of practice. Mystical experiences, as well as psychological conditions, such as anxiety and depression, were measured.

RESULTS: Participants in the EcoMeditation group showed significantly increased connectivity between the bilateral hippocampus and the bilateral insula, compared to pre-intervention. In addition, significant decreases of connectivity between the bilateral hippocampus and the midprefrontal and left dorsolateral prefrontal cortices occurred. EcoMeditation participants also scored significantly higher for mystical experiences than the control group. The results for emotional states were mixed, with one assessment finding increased positive mood, but another finding increased negative affect.

CONCLUSION: After only four weeks, participants using EcoMeditation demonstrated brain states similar to meditation adepts with thousands of hours of traditional practice.

PMID:36204165 | PMC:PMC9507148

Magnetic resonance imaging of the dopamine system in schizophrenia - A scoping review

Fri, 10/07/2022 - 10:00

Front Psychiatry. 2022 Sep 20;13:925476. doi: 10.3389/fpsyt.2022.925476. eCollection 2022.

ABSTRACT

For decades, aberrant dopamine transmission has been proposed to play a central role in schizophrenia pathophysiology. These theories are supported by human in vivo molecular imaging studies of dopamine transmission, particularly positron emission tomography. However, there are several downsides to such approaches, for example limited spatial resolution or restriction of the measurement to synaptic processes of dopaminergic neurons. To overcome these limitations and to measure complementary aspects of dopamine transmission, magnetic resonance imaging (MRI)-based approaches investigating the macrostructure, metabolism, and connectivity of dopaminergic nuclei, i.e., substantia nigra pars compacta and ventral tegmental area, can be employed. In this scoping review, we focus on four dopamine MRI methods that have been employed in patients with schizophrenia so far: neuromelanin MRI, which is thought to measure long-term dopamine function in dopaminergic nuclei; morphometric MRI, which is assumed to measure the volume of dopaminergic nuclei; diffusion MRI, which is assumed to measure fiber-based structural connectivity of dopaminergic nuclei; and resting-state blood-oxygenation-level-dependent functional MRI, which is thought to measure functional connectivity of dopaminergic nuclei based on correlated blood oxygenation fluctuations. For each method, we describe the underlying signal, outcome measures, and downsides. We present the current state of research in schizophrenia and compare it to other disorders with either similar (psychotic) symptoms, i.e., bipolar disorder and major depressive disorder, or dopaminergic abnormalities, i.e., substance use disorder and Parkinson's disease. Finally, we discuss overarching issues and outline future research questions.

PMID:36203848 | PMC:PMC9530597 | DOI:10.3389/fpsyt.2022.925476

Alteration of resting-state functional connectivity network properties in patients with social anxiety disorder after virtual reality-based self-training

Fri, 10/07/2022 - 10:00

Front Psychiatry. 2022 Sep 20;13:959696. doi: 10.3389/fpsyt.2022.959696. eCollection 2022.

ABSTRACT

Social anxiety disorder (SAD) is a mental disorder characterized by excessive anxiety in social situations. This study aimed to examine the alteration of resting-state functional connectivity in SAD patients related to the virtual reality-based self-training (VRS) which enables exposure to social situations in a controlled environment. Fifty-two SAD patients were randomly assigned to the experimental group who received the VRS, or the control group who did not. Self-report questionnaires and resting-state functional magnetic resonance imaging (fMRI) were performed to assess clinical symptoms and analyze the resting-state network properties, respectively. Significant decrease in social anxiety and an increase in self-esteem was found in the experimental group. From the resting-state fMRI analysis, alteration of local network properties in the left dorsolateral prefrontal gyrus (-10.0%, p = 0.025), left inferior frontal gyrus (-32.3%, p = 0.044), left insula (-17.2%, p = 0.046), left Heschl's gyrus (-21.2%, p = 0.011), bilateral inferior temporal gyrus (right: +122.6%, p = 0.045; left:-46.7%, p = 0.015), and right calcarine sulcus (+17.0%, p = 0.010) were found in the experimental group. Average shortest path length (+8.3%, p = 0.008) and network efficiency (-7.6%, p = 0.011) are found to be altered from the global network property analysis. In addition, the experimental group displayed more positive and more negative changes in the correlation trend of average shortest path length (p = 0.004) and global network efficiency (p = 0.014) with the severity of social anxiety, respectively. These results suggest potential effectiveness of the VRS, which is possibly related to the change of aberrant processing and control of visual and auditory linguistic stimuli and the adaptive change in rumination pattern.

PMID:36203841 | PMC:PMC9530634 | DOI:10.3389/fpsyt.2022.959696

Dynamic connectivity patterns of resting-state brain functional networks in healthy individuals after acute alcohol intake

Fri, 10/07/2022 - 10:00

Front Neurosci. 2022 Sep 20;16:974778. doi: 10.3389/fnins.2022.974778. eCollection 2022.

ABSTRACT

AIMS: Currently, there are only a few studies concerning brain functional alterations after acute alcohol exposure, and the majority of existing studies attach more importance to the spatial properties of brain function without considering the temporal properties. The current study adopted sliding window to investigate the resting-state brain networks in healthy volunteers after acute alcohol intake and to explore the dynamic changes in network connectivity.

MATERIALS AND METHODS: Twenty healthy volunteers were enrolled in this study. Blood-oxygen-level-dependent (BOLD) data prior to drinking were obtained as control, while that 0.5 and 1 h after drinking were obtained as the experimental group. Reoccurring functional connectivity patterns (states) were determined following group independent component analysis (ICA), sliding window analysis and k-means clustering. Between-group comparisons were performed with respect to the functional connectivity states fractional windows, mean dwell time, and the number of transitions.

RESULTS: Three optimal functional connectivity states were identified. The fractional windows and mean dwell time of 0.5 h group and 1 h group increased in state 3, while the fraction window and mean dwell time of 1 h group decreased in state 1. State 1 is characterized by strong inter-network connections between basal ganglia network (BGN) and sensorimotor network (SMN), BGN and cognitive executive network (CEN), and default mode network (DMN) and visual network (VN). However, state 3 is distinguished by relatively weak intra-network connections in SMN, VN, CEN, and DMN. State 3 was thought to be a characteristic connectivity pattern of the drunk brain. State 1 was believed to represent the brain's main connection pattern when awake. Such dynamic changes in brain network connectivity were consistent with participants' subjective feelings after drinking.

CONCLUSION: The current study reveals the dynamic change in resting-state brain functional network connectivity before and after acute alcohol intake. It was discovered that there might be relatively independent characteristic functional network connection patterns under intoxication, and the corresponding patterns characterize the clinical manifestations of volunteers. As a valuable imaging biomarker, dynamic functional network connectivity (dFNC) offers a new approach and basis for further explorations on brain network alterations after alcohol consumption and the alcohol-related mechanisms for neurological damage.

PMID:36203810 | PMC:PMC9531019 | DOI:10.3389/fnins.2022.974778

Altered mean apparent propagator-based microstructure and the corresponding functional connectivity of the parahippocampus and thalamus in Crohn's disease

Fri, 10/07/2022 - 10:00

Front Neurosci. 2022 Sep 20;16:985190. doi: 10.3389/fnins.2022.985190. eCollection 2022.

ABSTRACT

Crohn's disease (CD) is a chronic and relapsing inflammatory bowel disorder that has been shown to generate neurological impairments, which has the potential to signify disease activity in an underlying neurological manner. The objective of this study was to investigate the abnormalities of brain microstructure and the corresponding functional connectivity (FC) in patients with CD, as well as their associations with disease condition. Twenty-two patients with CD and 22 age-, gender-, and education-matched healthy controls (HCs) were enrolled in this study. All subjects underwent mean apparent propagator (MAP)-MRI and resting-state functional magnetic resonance imaging (MRI) (rs-fMRI) data collection. Each patient was evaluated clinically for the condition and duration of the disease. The MAP metrics were extracted and compared between two groups. Pearson's correlation analysis was conducted to determine the relationship between disease characteristics and significantly abnormal MAP metrics in the CD group. Regions of interest (ROIs) for ROI-wise FC analysis were selected based on their correlation with MAP metrics. Results showed that multiple brain regions, including the parahippocampus and thalamus, exhibited statistically significant differences in MAP metrics between CD patients and HCs. Additionally, CD patients exhibited decreased FC between the left parahippocampus and bilateral thalamus, as well as the right parahippocampus and bilateral thalamus. The findings of this work provide preliminary evidence that structural abnormalities in the parahippocampal gyrus (PHG) and thalamus, as well as decreased FC between them, may reflect the degree of inflammatory of the disease and serve as brain biomarkers for evaluating CD activity.

PMID:36203806 | PMC:PMC9530355 | DOI:10.3389/fnins.2022.985190

Brain magnetic resonance imaging findings among children with epilepsy in two urban hospital settings, Kampala-Uganda: a descriptive study

Thu, 10/06/2022 - 10:00

BMC Med Imaging. 2022 Oct 6;22(1):175. doi: 10.1186/s12880-022-00901-7.

ABSTRACT

BACKGROUND: Epilepsy is one of the most common neurological conditions in children worldwide. Its presentation is heterogeneous, with diverse underlying aetiology, clinical presentation, and prognosis. Structural brain abnormalities are among the recognized causes of epilepsy. Brain Magnetic Resonance Imaging (MRI) is the imaging modality of choice for epilepsy workup. We aimed to determine the prevalence and describe the structural abnormalities identified in the brain MRI studies performed on children with epilepsy from two urban hospitals in Kampala, Uganda.

METHODS: This was a cross-sectional descriptive study performed at two urban hospital MRI centres. The study population was 147 children aged 1 day to 17 years with confirmed epilepsy. Brain MRI was performed for each child and a questionnaire was used to collect clinical data.

RESULTS: The prevalence of structural abnormalities among children with epilepsy was 74.15% (109 out of 147). Of these, 68.81% were male, and the rest were female. Among these, the majority, 40.14% (59 of 144) were aged 1 month to 4 years. Acquired structural brain abnormalities were the commonest at 69.22% with hippocampal sclerosis (HS) leading while disorders of cortical development were the most common congenital causes. An abnormal electroencephalogram (EEG) was significant for brain MRI abnormalities among children with epilepsy with 95% of participants with an abnormal EEG study having epileptogenic structural abnormalities detected in their brain MRI studies.

CONCLUSION AND RECOMMENDATION: Two-thirds of children with epilepsy had structural brain abnormalities. Abnormal activity in the EEG study was found to positively correlate with abnormal brain MRI findings. As such, EEG study should be considered where possible before MRI studies as a determinant for children with epilepsy who will be having imaging studies done in the Ugandan setting.

PMID:36203127 | DOI:10.1186/s12880-022-00901-7

Deconstructing dissociation: a triple network model of trauma-related dissociation and its subtypes

Thu, 10/06/2022 - 10:00

Neuropsychopharmacology. 2022 Oct 6. doi: 10.1038/s41386-022-01468-1. Online ahead of print.

ABSTRACT

Trauma-related pathological dissociation is characterized by disruptions in one's sense of self, perceptual, and affective experience. Dissociation and its trauma-related antecedents disproportionately impact women. However, despite the gender-related prevalence and high individual and societal costs, dissociation remains widely underappreciated in clinical practice. Moreover, dissociation lacks a synthesized neurobiological model across its subtypes. Leveraging the Triple Network Model of psychopathology, we sought to parse heterogeneity in dissociative experience by examining functional connectivity of three core neurocognitive networks as related to: (1) the dimensional dissociation subtypes of depersonalization/derealization and partially-dissociated intrusions; and, (2) the diagnostic category of dissociative identity disorder (DID). Participants were 91 women with and without: a history of childhood trauma, current posttraumatic stress disorder (PTSD), and varied levels of dissociation. Participants provided clinical data about dissociation, PTSD symptoms, childhood maltreatment history, and completed a resting-state functional magnetic resonance imaging scan. We used a novel statistical approach to assess both overlapping and unique contributions of dissociation subtypes. Covarying for age, childhood maltreatment and PTSD severity, we found dissociation was linked to hyperconnectivity within central executive (CEN), default (DN), and salience networks (SN), and decreased connectivity of CEN and SN with other areas. Moreover, we isolated unique connectivity markers associated with depersonalization/derealization in CEN and DN, to partially-dissociated intrusions in CEN, and to DID in CEN. This suggests dissociation subtypes have robust functional connectivity signatures that may serve as targets for PTSD/DID treatment engagement. Our findings underscore dissociation assessment as crucial in clinical care, in particular, to reduce gender-related health disparities.

PMID:36202907 | DOI:10.1038/s41386-022-01468-1

Reliability and Similarity of Resting State Functional Connectivity Networks Imaged Using Wearable, High-Density Diffuse Optical Tomography in the Home Setting

Thu, 10/06/2022 - 10:00

Neuroimage. 2022 Oct 3:119663. doi: 10.1016/j.neuroimage.2022.119663. Online ahead of print.

ABSTRACT

BACKGROUND: Demonstrating the similarity across sessions and reliability across different scan durations of the brain's resting state functional connectivity (RSFC) networks is essential for validating results and possibly minimizing the scanning time needed to obtain stable measures of RSFC. Recent advances in optical functional neuroimaging technologies have resulted in fully wearable devices that may serve as a complimentary tool to functional magnetic resonance imaging (fMRI) and allow for investigations of RSFC networks repeatedly and easily in non-traditional scanning environments.

METHODS: Resting-state cortical hemodynamic activity was repeatedly measured in a single individual in a home environment during COVID-19 lockdown conditions using the first ever application of a 24-module (72 sources, 96 detectors), wearable high-density diffuse optical tomography (HD-DOT) system. Twelve-minute recordings of resting-state data were acquired over the pre-frontal and occipital regions in fourteen experimental sessions over three weeks. As an initial validation of the data, spatial independent component analysis was used to identify RSFC networks. Reliability and similarity scores were computed using metrics adapted from the fMRI literature.

RESULTS: We observed RSFC networks over visual regions (visual peripheral, visual central networks) and higher-order association regions (control, salience and default mode network), consistent with previous literature. High similarity was observed across testing sessions and across chromophores (oxygenated and deoxygenated haemoglobin, HbO and HbR) for all functional networks, and for each network considered separately. Stable reliability values (described here as a <10% change between time windows) were obtained for HbO and HbR with differences on required scanning time observed on a network-by-network basis.

DISCUSSION: Using RSFC data from a highly sampled individual, the present work demonstrates that wearable HD-DOT can be used to obtain RSFC measurements with high similarity and reliability across imaging sessions and recording durations in the home environment. Wearable HD-DOT may serve as a complimentary tool to fMRI for studying RSFC networks outside of the traditional scanning environment and in vulnerable populations for whom fMRI is not feasible.

PMID:36202159 | DOI:10.1016/j.neuroimage.2022.119663

A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia

Thu, 10/06/2022 - 10:00

IEEE J Biomed Health Inform. 2022 Oct 6;PP. doi: 10.1109/JBHI.2022.3212479. Online ahead of print.

ABSTRACT

Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.

PMID:36201411 | DOI:10.1109/JBHI.2022.3212479

Machine learning evaluation of LV outflow obstruction in hypertrophic cardiomyopathy using three-chamber cardiovascular magnetic resonance

Thu, 10/06/2022 - 10:00

Int J Cardiovasc Imaging. 2022 Oct 6. doi: 10.1007/s10554-022-02724-7. Online ahead of print.

ABSTRACT

Left ventricular outflow tract obstruction (LVOTO) is common in hypertrophic cardiomyopathy (HCM), but relationships between anatomical metrics and obstruction are poorly understood. We aimed to develop machine learning methods to evaluate LVOTO in HCM patients and quantify relationships between anatomical metrics and obstruction. This retrospective analysis of 1905 participants of the HCM Registry quantified 11 anatomical metrics derived from 14 landmarks automatically detected on the three-chamber long axis cine CMR images. Linear and logistic regression was used to quantify strengths of relationships with the presence of LVOTO (defined by resting Doppler pressure drop of > 30 mmHg), using the area under the receiver operating characteristic (AUC). Intraclass correlation coefficients between the network predictions and three independent observers showed similar agreement to that between observers. The distance from anterior mitral valve leaflet tip to basal septum (AML-BS) was most highly correlated with Doppler pressure drop (R2 = 0.19, p < 10-5). Multivariate stepwise regression found the best predictive model included AML-BS, AML length to aortic valve diameter ratio, AML length to LV width ratio, and midventricular septal thickness metrics (AUC 0.84). Excluding AML-BS, metrics grouped according to septal hypertrophy, LV geometry, and AML anatomy each had similar associations with LVOTO (AUC 0.71, 0.71, 0.68 respectively, p = ns), significantly less than their combination (AUC 0.77, p < 0.05 for each). Anatomical metrics derived from a standard three-chamber CMR cine acquisition can be used to highlight risk of LVOTO, and suggest further investigation if necessary. A combination of geometric factors is required to provide the best risk prediction.

PMID:36201099 | DOI:10.1007/s10554-022-02724-7

Cerebellar Functional Dysconnectivity in Drug-Naïve Patients With First-Episode Schizophrenia

Thu, 10/06/2022 - 10:00

Schizophr Bull. 2022 Oct 6:sbac121. doi: 10.1093/schbul/sbac121. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebellar functional dysconnectivity has long been implicated in schizophrenia. However, the detailed dysconnectivity pattern and its underlying biological mechanisms have not been well-charted. This study aimed to conduct an in-depth characterization of cerebellar dysconnectivity maps in early schizophrenia.

STUDY DESIGN: Resting-state fMRI data were processed from 196 drug-naïve patients with first-episode schizophrenia and 167 demographically matched healthy controls. The cerebellum was parcellated into nine functional systems based on a state-of-the-art atlas, and seed-based connectivity for each cerebellar system was examined. The observed connectivity alterations were further associated with schizophrenia risk gene expressions using data from the Allen Human Brain Atlas.

STUDY RESULTS: Overall, we observed significantly increased cerebellar connectivity with the sensorimotor cortex, default-mode regions, ventral part of visual cortex, insula, and striatum. In contrast, decreased connectivity was shown chiefly within the cerebellum, and between the cerebellum and the lateral prefrontal cortex, temporal lobe, and dorsal visual areas. Such dysconnectivity pattern was statistically similar across seeds, with no significant group by seed interactions identified. Moreover, connectivity strengths of hypoconnected but not hyperconnected regions were significantly correlated with schizophrenia risk gene expressions, suggesting potential genetic underpinnings for the observed hypoconnectivity.

CONCLUSIONS: These findings suggest a common bidirectional dysconnectivity pattern across different cerebellar subsystems, and imply that such bidirectional alterations may relate to different biological mechanisms.

PMID:36200880 | DOI:10.1093/schbul/sbac121

Probing cellular health at the muscle level-Multi-frequency bioimpedance in Parkinson's disease

Thu, 10/06/2022 - 10:00

Physiol Rep. 2022 Oct;10(19):e15465. doi: 10.14814/phy2.15465.

ABSTRACT

Bioimpedance (mfBIA) non-invasively assesses cellular muscle health. Our aim was to explore whether mfBIA captures abnormal cellular muscle health in patients with Parkinson's Disease (PD) and how such changes are modulated with the use of Parkinson's medication. In patients with PD (n = 20) mfBIA measurements were made of biceps brachii, triceps, and extensor carpi radialis longus muscles of the more affected arm whilst at rest, using a mobile mfBIA device (IMPEDIMED, Australia). mfBIA and assessment of motor symptoms were performed in a pragmatic off-medication state, as well as one and 3 h after oral intake of 200 mg levodopa. Age and sex-matched healthy subjects (HC; n = 20) served as controls. PD and HC mfBIA parameters were compared by applying an unpaired two-tailed adjusted t-test and ANOVA with Tukey's correction for multiple comparisons (p ≤ 0.05). The PD group consisted of 13 men (71 ± 17 years) and 7 women (65 ± 7 years). Independent of medication, internal (Ri ) and external resistance (Re ) were found to be significantly higher, and membrane capacitance (Mc) significantly lower, in m.biceps brachii in PD subjects compared to HC. Center frequency (fc) was significantly higher in m.biceps brachii of PD subjects in the "medication-off" state. There was no difference between PD and HC in mfBIA parameters in the measured extensor muscles. The upper limb flexor muscle shows a difference in mfBIA parameters in PD compared to HC. mfBIA may be useful in the diagnosis and assessment of PD patients and is objective, non-invasive, reliable, and easy to use.

PMID:36200221 | DOI:10.14814/phy2.15465

Changes and Influencing Factors of Stress Disorder in Patients with Mild Traumatic Brain Injury Stress Disorder

Thu, 10/06/2022 - 10:00

Biomed Res Int. 2022 Sep 26;2022:9082946. doi: 10.1155/2022/9082946. eCollection 2022.

ABSTRACT

Traumatic brain injury (TBI) is a brain injury caused by motor vehicle accidents, falls from heights, sports, and combat. Posttraumatic stress disorder (PTSD) is a complex mental disorder caused by physical and psychological trauma, which manifests itself with symptoms such as anxiety, depression, and cognitive dysfunction. How its symptoms arise and what factors influence it are not fully understood nor can it be predicted. In order to better understand the changes after stress disorder in TBI patients and the influencing factors of PTSD, this paper analyzed the changes and influencing factors of stress disorder in patients with mild traumatic brain injury stress disorder. In this paper, the Wechsler Memory Scale and functional magnetic resonance imaging were first used to study the memory impairment and functional changes of corresponding brain regions in patients with TBI stress disorder, and then, the Pittsburgh Sleep Quality Index Scale and the pain Visual Analogue Scale were used to study the influencing factors of PTSD. The results of the study showed that PTSD patients reduced and enhanced regional brain functional activity and impaired memory function in the resting state. Male gender, age under 45 years, no hemiplegia, and good sleep quality were protective factors for PTSD in TBI patients. The need for drug-assisted sleep, severe headache, and moderate headache was the risk factor for PTSD in TBI patients.

PMID:36199756 | PMC:PMC9529484 | DOI:10.1155/2022/9082946

Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder

Thu, 10/06/2022 - 10:00

Neural Plast. 2022 Sep 26;2022:4714763. doi: 10.1155/2022/4714763. eCollection 2022.

ABSTRACT

Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.

PMID:36199291 | PMC:PMC9529483 | DOI:10.1155/2022/4714763

Associations of resting heart rate with incident dementia, cognition, and brain structure: a prospective cohort study of UK biobank

Wed, 10/05/2022 - 10:00

Alzheimers Res Ther. 2022 Oct 5;14(1):147. doi: 10.1186/s13195-022-01088-3.

ABSTRACT

BACKGROUND: Resting heart rate (RHR) has been linked with an increased risk of dementia. However, evidence characterizing the associations of RHR with different dementia subtypes and their underlying mechanisms remains scarce. This study aims to investigate the relationships of RHR with different dementia types, cognitive function, and brain structural abnormalities.

METHODS: Three hundred thirty-nine thousand nine hundred one participants with no prior diagnosis of dementia from the UK biobank were analyzed. Cox regression and restricted cubic spline models examined the associations between RHR with all-cause dementia (ACD) and its major subtypes-Alzheimer's disease (AD) and vascular dementia (VaD). Logistic regression models assessed the associations of RHR with cognitive function, and linear regression models estimated the associations with hippocampal subfield volume and white matter tract integrity indexed by magnetic resonance imaging data.

RESULTS: During an average of 3148 (± 941.08) days of follow-up, 4177 individuals were diagnosed with dementia, including 2354 AD and 989 VaD cases. RHR ≥ 80bpm was associated with ACD (HR: 1.18, 95% CI: 1.08-1.28, P < 0.001) and VaD (HR: 1.29, 95% CI: 1.08-1.54, P = 0.005) but not AD in multi-adjusted models. A 10-bpm increment of RHR demonstrated non-linear effects in VaD, consisting of J-shape relationships. Several heterogeneities were indicated in stratified analysis, in which RHR measures only showed associations with dementia incidents in relatively younger populations (age ≤ 65) and females. Apart from dementia analysis, elevated RHR was associated with worsening performance in fluid intelligence and reaction time of cognitive tasks, decreased hippocampal subfields volume, and poor white matter tract integrity.

CONCLUSIONS: RHR is associated with increased risks of ACD and VaD but also presented with few heterogeneities across different sex and age groups. Elevated RHR also appears to have deleterious effects on cognitive function and is distinctively associated with volume reduction in hippocampal subfields and impaired white matter tract integrity.

PMID:36199126 | DOI:10.1186/s13195-022-01088-3

Frontal lobe fALFF measured from resting-state fMRI as a prognostic biomarker in first-episode psychosis

Wed, 10/05/2022 - 10:00

Neuropsychopharmacology. 2022 Oct 5. doi: 10.1038/s41386-022-01470-7. Online ahead of print.

ABSTRACT

Clinical response to antipsychotic drug treatment is highly variable, yet prognostic biomarkers are lacking. The goal of the present study was to test whether the fractional amplitude of low-frequency fluctuations (fALFF), as measured from baseline resting-state fMRI data, can serve as a potential biomarker of treatment response to antipsychotics. Patients in the first episode of psychosis (n = 126) were enrolled in two prospective studies employing second-generation antipsychotics (risperidone or aripiprazole). Patients were scanned at the initiation of treatment on a 3T MRI scanner (Study 1, GE Signa HDx, n = 74; Study 2, Siemens Prisma, n = 52). Voxelwise fALFF derived from baseline resting-state fMRI scans served as the primary measure of interest, providing a hypothesis-free (as opposed to region-of-interest) search for regions of the brain that might be predictive of response. At baseline, patients who would later meet strict criteria for clinical response (defined as two consecutive ratings of much or very much improved on the CGI, as well as a rating of ≤3 on psychosis-related items of the BPRS-A) demonstrated significantly greater baseline fALFF in bilateral orbitofrontal cortex compared to non-responders. Thus, spontaneous activity in orbitofrontal cortex may serve as a prognostic biomarker of antipsychotic treatment.

PMID:36198875 | DOI:10.1038/s41386-022-01470-7

Subjective cognitive decline predicts lower cingulo-opercular network functional connectivity in individuals with lower neurite density in the forceps minor: Cingulo-opercular network in SCD

Wed, 10/05/2022 - 10:00

Neuroimage. 2022 Oct 2:119662. doi: 10.1016/j.neuroimage.2022.119662. Online ahead of print.

ABSTRACT

Cognitive complaints of attention/concentration problems are highly frequent in older adults with subjective cognitive decline (SCD). Functional connectivity in the cingulo-opercular network (CON-FC) supports cognitive control, tonic alertness, and visual processing speed. Thus, those complaints in SCD may reflect a decrease in CON-FC. Frontal white-matter tracts such as the forceps minor exhibit age- and SCD-related alterations and, therefore, might influence the CON-FC decrease in SCD. Here, we aimed to determine whether SCD predicts an impairment in CON-FC and whether neurite density in the forceps minor modulates that effect. To do so, we integrated cross-sectional and longitudinal analyses of multimodal data in a latent growth curve modeling approach. Sixty-nine healthy older adults (13 males; 68.33 ± 7.95 years old) underwent resting-state functional and diffusion-weighted magnetic resonance imaging, and the degree of SCD was assessed at baseline with the memory functioning questionnaire (greater score indicating more SCD). Forty-nine of the participants were further enrolled in two follow-ups, each about 18 months apart. Baseline SCD did not predict CON-FC after three years or its rate of change (p-values > 0.092). Notably, however, the forceps minor neurite density did modulate the relation between SCD and CON-FC (intercept; b = 0.21, 95% confidence interval, CI, [0.03, 0.39], p = 0.021), so that SCD predicted a greater CON-FC decrease in older adults with relatively lower neurite density in the forceps minor. The neurite density of the forceps minor, in turn, negatively correlated with age. These results suggest that CON-FC alterations in SCD are dependent upon the forceps minor neurite density. Accordingly, these results imply modifiable age-related factors that could help delay or mitigate both age and SCD-related effects on brain connectivity.

PMID:36198354 | DOI:10.1016/j.neuroimage.2022.119662

Pages