New resting-state fMRI related studies at PubMed

Identification of chronic mild traumatic brain injury using resting state functional MRI and machine learning techniques

Thu, 01/26/2023 - 11:00

Front Neurosci. 2023 Jan 9;16:1099560. doi: 10.3389/fnins.2022.1099560. eCollection 2022.


Mild traumatic brain injury (mTBI) is a major public health concern that can result in a broad spectrum of short-term and long-term symptoms. Recently, machine learning (ML) algorithms have been used in neuroscience research for diagnostics and prognostic assessment of brain disorders. The present study aimed to develop an automatic classifier to distinguish patients suffering from chronic mTBI from healthy controls (HCs) utilizing multilevel metrics of resting-state functional magnetic resonance imaging (rs-fMRI). Sixty mTBI patients and forty HCs were enrolled and allocated to training and testing datasets with a ratio of 80:20. Several rs-fMRI metrics including fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), functional connectivity strength (FCS), and seed-based FC were generated from two main analytical categories: local measures and network measures. Statistical two-sample t-test was employed comparing between mTBI and HCs groups. Then, for each rs-fMRI metric the features were selected extracting the mean values from the clusters showing significant differences. Finally, the support vector machine (SVM) models based on separate and multilevel metrics were built and the performance of the classifiers were assessed using five-fold cross-validation and via the area under the receiver operating characteristic curve (AUC). Feature importance was estimated using Shapley additive explanation (SHAP) values. Among local measures, the range of AUC was 86.67-100% and the optimal SVM model was obtained based on combined multilevel rs-fMRI metrics and DC as a separate model with AUC of 100%. Among network measures, the range of AUC was 80.42-93.33% and the optimal SVM model was obtained based on the combined multilevel seed-based FC metrics. The SHAP analysis revealed the DC value in the left postcentral and seed-based FC value between the motor ventral network and right superior temporal as the most important local and network features with the greatest contribution to the classification models. Our findings demonstrated that different rs-fMRI metrics can provide complementary information for classifying patients suffering from chronic mTBI. Moreover, we showed that ML approach is a promising tool for detecting patients with mTBI and might serve as potential imaging biomarker to identify patients at individual level.

CLINICAL TRIAL REGISTRATION: [], identifier [NCT03241732].

PMID:36699521 | PMC:PMC9869678 | DOI:10.3389/fnins.2022.1099560

Assessing atypical brain functional connectivity development: An approach based on generative adversarial networks

Thu, 01/26/2023 - 11:00

Front Neurosci. 2023 Jan 9;16:1025492. doi: 10.3389/fnins.2022.1025492. eCollection 2022.


Generative Adversarial Networks (GANs) are promising analytical tools in machine learning applications. Characterizing atypical neurodevelopmental processes might be useful in establishing diagnostic and prognostic biomarkers of psychiatric disorders. In this article, we investigate the potential of GANs models combined with functional connectivity (FC) measures to build a predictive neurotypicality score 3-years after scanning. We used a ROI-to-ROI analysis of resting-state functional magnetic resonance imaging (fMRI) data from a community-based cohort of children and adolescents (377 neurotypical and 126 atypical participants). Models were trained on data from neurotypical participants, capturing their sample variability of FC. The discriminator subnetwork of each GAN model discriminated between the learned neurotypical functional connectivity pattern and atypical or unrelated patterns. Discriminator models were combined in ensembles, improving discrimination performance. Explanations for the model's predictions are provided using the LIME (Local Interpretable Model-Agnostic) algorithm and local hubs are identified in light of these explanations. Our findings suggest this approach is a promising strategy to build potential biomarkers based on functional connectivity.

PMID:36699518 | PMC:PMC9868740 | DOI:10.3389/fnins.2022.1025492

Changes in the structure, perfusion, and function of the hippocampus in type 2 diabetes mellitus

Thu, 01/26/2023 - 11:00

Front Neurosci. 2023 Jan 9;16:1070911. doi: 10.3389/fnins.2022.1070911. eCollection 2022.


OBJECTIVE: This study aims to explore the changes in the structure, perfusion, and function of the bilateral hippocampus in type 2 diabetes mellitus (T2DM) applying multimodal MRI methods, hoping to provide reliable neuroimaging evidence for the diagnosis of hippocampus-related brain injury in T2DM.

METHODS: We recruited 30 T2DM patients and 45 healthy controls (HCs), on which we performed 3D T1-weighted images, resting-state functional MRI (rs-fMRI), arterial spin labeling (ASL) sequences, and a series of cognitive tests. Then, we compared the differences of two groups in the cerebral blood flow (CBF) value, amplitude of low-frequency fluctuation (ALFF) value, fractional ALFF (fALFF) value, coherence-based regional homogeneity (Cohe-ReHo) value, and degree centrality (DC) values of the bilateral hippocampus.

RESULTS: In the T2DM group, the bilateral hippocampal volumes and the CBF value of the right hippocampus were lower than those in the HCs, while the ALFF value, fALFF value, and Cohe-ReHo value of the bilateral hippocampus were higher than those in the HCs. Correlation analysis showed that fasting blood glucose (FBG) was negatively correlated with the residuals of left hippocampal volume (r = -0.407, P = 0.025) and right hippocampal volume (r = -0.420, P = 0.021). The residual of the auditory-verbal learning test (AVLT) (immediate) score was positively correlated with the residual of right hippocampal volume (r = 0.369, P = 0.045).

CONCLUSION: This study indicated that the volume and perfusion of the hippocampus are decreased in T2DM patients that related to chronic hyperglycemia. Local spontaneous neural activity and coordination are increased in the hippocampus of T2DM patients, possibly as an adaptive compensation for cognitive decline.

PMID:36699515 | PMC:PMC9868830 | DOI:10.3389/fnins.2022.1070911

Alterations of degree centrality and functional connectivity in classic trigeminal neuralgia

Thu, 01/26/2023 - 11:00

Front Neurosci. 2023 Jan 9;16:1090462. doi: 10.3389/fnins.2022.1090462. eCollection 2022.


OBJECTIVES: Recent neuroimaging studies have indicated a wide range of structural and regional functional alterations in patients with classic trigeminal neuralgia (CTN). However, few studies have focused on the intrinsic functional characteristics of network organization in the whole brain. Therefore, the present study aimed to characterize the potential intrinsic dysconnectivity pattern of the whole brain functional networks at the voxel level using the degree centrality (DC) analysis in CTN patients.

METHODS: Thirty-four patients with CTN and twenty-nine well-matched healthy controls (HCs) participated in this study. All subjects underwent resting-state functional magnetic resonance imaging (rs-MRI) examination and clinical and neuropsychologic assessments. DC is a graph theory-based measurement that represents the overall functional connectivity (FC) numbers between one voxel and other brain voxels. We first investigated brain regions exhibiting abnormal DC, and further identified their perturbation on FC with other brain regions using a seed-based FC analysis in patients with CTN. In addition, correlation analyses were performed to evaluate the relationship between the abnormal DC value and clinical variables in CTN patients.

RESULTS: Compared with the HCs, the patients with CTN exhibited significantly greater DC values in the right pallidum and right putamen, and lower DC values in the right lingual gyrus, right calcarine sulcus, left paracentral lobule, and left midcingulate cortex. A further seed-based FC analysis revealed that the right lingual gyrus showed decreased FC within the visual network and with other core brain networks, including the sensorimotor network, default mode network, and salience network, relative to HCs. Additionally, the left midcingulate cortex exhibited decreased FC within the middle cingulate cortex and the visual network in CTN patients. Moreover, the DC value in the left midcingulate cortex was negatively correlated with the illness duration.

CONCLUSION: The present study shows that CTN patients exhibited specific functional connectivity network alterations in the basal ganglia, visual network, and salience network, which may reflect the aberrant neural network communication in pain processing and modulation. These findings may provide novel insight for understanding the mechanisms of pain chronicity in CTN patients.

PMID:36699513 | PMC:PMC9870176 | DOI:10.3389/fnins.2022.1090462

Biomarkers for prognostic functional recovery poststroke: A narrative review

Thu, 01/26/2023 - 11:00

Front Cell Dev Biol. 2023 Jan 9;10:1062807. doi: 10.3389/fcell.2022.1062807. eCollection 2022.


Background and objective: Prediction of poststroke recovery can be expressed by prognostic biomarkers that are related to the pathophysiology of stroke at the cellular and molecular level as well as to the brain structural and functional reserve after stroke at the systems neuroscience level. This study aimed to review potential biomarkers that can predict poststroke functional recovery. Methods: A narrative review was conducted to qualitatively summarize the current evidence on biomarkers used to predict poststroke functional recovery. Results: Neurophysiological measurements and neuroimaging of the brain and a wide diversity of molecules had been used as prognostic biomarkers to predict stroke recovery. Neurophysiological studies using resting-state electroencephalography (EEG) revealed an interhemispheric asymmetry, driven by an increase in low-frequency oscillation and a decrease in high-frequency oscillation in the ipsilesional hemisphere relative to the contralesional side, which was indicative of individual recovery potential. The magnitude of somatosensory evoked potentials and event-related desynchronization elicited by movement in task-related EEG was positively associated with the quantity of recovery. Besides, transcranial magnetic stimulation (TMS) studies revealed the potential values of using motor-evoked potentials (MEP) and TMS-evoked EEG potentials from the ipsilesional motor cortex as prognostic biomarkers. Brain structures measured using magnetic resonance imaging (MRI) have been implicated in stroke outcome prediction. Specifically, the damage to the corticospinal tract (CST) and anatomical motor connections disrupted by stroke lesion predicted motor recovery. In addition, a wide variety of molecular, genetic, and epigenetic biomarkers, including hemostasis, inflammation, tissue remodeling, apoptosis, oxidative stress, infection, metabolism, brain-derived, neuroendocrine, and cardiac biomarkers, etc., were associated with poor functional outcomes after stroke. However, challenges such as mixed evidence and analytical concerns such as specificity and sensitivity have to be addressed before including molecular biomarkers in routine clinical practice. Conclusion: Potential biomarkers with prognostic values for the prediction of functional recovery after stroke have been identified; however, a multimodal approach of biomarkers for prognostic prediction has rarely been studied in the literature. Future studies may incorporate a combination of multiple biomarkers from big data and develop algorithms using data mining methods to predict the recovery potential of patients after stroke in a more precise way.

PMID:36699006 | PMC:PMC9868572 | DOI:10.3389/fcell.2022.1062807

Increased functional connectivity patterns in mild Alzheimer's disease: A rsfMRI study

Thu, 01/26/2023 - 11:00

Front Aging Neurosci. 2023 Jan 9;14:1037347. doi: 10.3389/fnagi.2022.1037347. eCollection 2022.


BACKGROUND: Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder. In view of our rapidly aging population, there is an urgent need to identify Alzheimer's disease (AD) at an early stage. A potential way to do so is by assessing the functional connectivity (FC), i.e., the statistical dependency between two or more brain regions, through novel analysis techniques.

METHODS: In the present study, we assessed the static and dynamic FC using different approaches. A resting state (rs)fMRI dataset from the Alzheimer's disease neuroimaging initiative (ADNI) was used (n = 128). The blood-oxygen-level-dependent (BOLD) signals from 116 regions of 4 groups of participants, i.e., healthy controls (HC; n = 35), early mild cognitive impairment (EMCI; n = 29), late mild cognitive impairment (LMCI; n = 30), and Alzheimer's disease (AD; n = 34) were extracted and analyzed. FC and dynamic FC were extracted using Pearson's correlation, sliding-windows correlation analysis (SWA), and the point process analysis (PPA). Additionally, graph theory measures to explore network segregation and integration were computed.

RESULTS: Our results showed a longer characteristic path length and a decreased degree of EMCI in comparison to the other groups. Additionally, an increased FC in several regions in LMCI and AD in contrast to HC and EMCI was detected. These results suggest a maladaptive short-term mechanism to maintain cognition.

CONCLUSION: The increased pattern of FC in several regions in LMCI and AD is observable in all the analyses; however, the PPA enabled us to reduce the computational demands and offered new specific dynamic FC findings.

PMID:36698861 | PMC:PMC9869068 | DOI:10.3389/fnagi.2022.1037347

Functional connectivity and mild behavioral impairment in dementia-free elderly

Thu, 01/26/2023 - 11:00

Alzheimers Dement (N Y). 2023 Jan 18;9(1):e12371. doi: 10.1002/trc2.12371. eCollection 2023.


BACKGROUND: Mild behavioral impairment (MBI) is a syndrome that uses later-life emergent and persistent neuropsychiatric symptoms (NPS) to identify a group at high risk for incident dementia. MBI is associated with neurodegenerative disease markers in advance of syndromic dementia. Functional connectivity (FC) correlates of MBI are understudied and could provide further insights into mechanisms early in the disease course. We used resting-state functional magnetic resonance imaging (rs-fMRI) to test the hypothesis that FC within the default mode network (DMN) and salience network (SN) of persons with MBI (MBI+) is reduced, relative to those without (MBI-).

METHODS: From two harmonized dementia-free cohort studies, using a score of ≥6 on the MBI Checklist to define MBI status, 32 MBI+ and 63 MBI- individuals were identified (mean age: 71.7 years; 54.7% female). Seed-based connectivity analysis was implemented in each MBI group using the CONN fMRI toolbox (v20.b), with the posterior cingulate cortex (PCC) as the seed region within the DMN and anterior cingulate cortex (ACC) as the seed within the SN. The average time series from the PCC and ACC were used to determine FC with other regions within the DMN (medial prefrontal cortex, lateral inferior parietal cortex) and SN (anterior insula, supramarginal gyrus, rostral prefrontal cortex), respectively. Age, sex, years of education, and Montreal Cognitive Assessment scores were included as model covariates. The false discovery rate approach was used to correct for multiple comparisons, with a p-value of .05 considered significant.

RESULTS: For the DMN, MBI+ individuals exhibited reduced FC between the PCC and the medial prefrontal cortex, compared to MBI-. For the SN, MBI+ individuals exhibited reduced FC between the ACC and left anterior insula.

CONCLUSION: MBI in dementia-free older adults is associated with reduced FC in networks known to be disrupted in dementia. Our results complement the evidence linking MBI with Alzheimer's disease biomarkers.

HIGHLIGHTS: Resting-state functional magnetic resonance imaging was completed in 95 dementia-free persons from FAVR and COMPASS-ND studies.Participants were stratified by informant-rated Mild Behavioral Impairment Checklist (MBI-C) score, ≥6 for MBI+.MBI+ participants showed reduced functional connectivity (FC) within the default mode network and salience network.These FC changes are consistent with those seen in early-stage Alzheimer's disease.MBI may help identify persons with early-stage neurodegenerative disease.

PMID:36698771 | PMC:PMC9847513 | DOI:10.1002/trc2.12371

MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models

Wed, 01/25/2023 - 11:00

Comput Med Imaging Graph. 2023 Jan 21;104:102187. doi: 10.1016/j.compmedimag.2023.102187. Online ahead of print.


Alcohol use disorder (AUD) is a complex condition representing a leading risk factor for death, disease and disability. Its high prevalence and severe health consequences make necessary a better understanding of the brain network alterations to improve diagnosis and treatment. The purpose of this study was to evaluate the potential of resting-state fMRI 3D texture features as a novel source of biomarkers to identify AUD brain network alterations following a radiomics approach. A longitudinal study was conducted in Marchigian Sardinian alcohol-preferring msP rats (N = 36) who underwent resting-state functional and structural MRI before and after 30 days of alcohol or water consumption. A cross-sectional human study was also conducted among 33 healthy controls and 35 AUD patients. The preprocessed functional data corresponding to control and alcohol conditions were used to perform a probabilistic independent component analysis, identifying seven independent components as resting-state networks. Forty-three radiomic features extracted from each network were compared using a Wilcoxon signed-rank test with Holm correction to identify the network most affected by alcohol consumption. Features extracted from this network were then used in the machine learning process, evaluating two feature selection methods and six predictive models within a nested cross-validation structure. The classification was evaluated by computing the area under the ROC curve. Images were quantized using different numbers of gray-levels to test their influence on the results. The influence of ageing, data preprocessing, and brain iron accumulation were also analyzed. The methodology was validated using structural scans. The striatal network in alcohol-exposed msP rats presented the most significant number of altered features. The radiomics approach supported this result achieving good classification performance in animals (AUC = 0.915 ± 0.100, with 12 features) and humans (AUC = 0.724 ± 0.117, with 9 features) using a random forest model. Using the structural scans, high accuracy was achieved with a multilayer perceptron in both species (animals: AUC > 0.95 with 2 features, humans: AUC > 0.82 with 18 features). The best results were obtained using a feature selection method based on the p-value. The proposed radiomics approach is able to identify AUD patients and alcohol-exposed rats with good accuracy, employing a subset of 3D features extracted from fMRI. Furthermore, it can help identify relevant networks in drug addiction.

PMID:36696812 | DOI:10.1016/j.compmedimag.2023.102187

Long-term ANT-DBS effects in pilocarpine-induced epileptic rats: A combined 9.4T MRI and histological study

Wed, 01/25/2023 - 11:00

J Neurosci Res. 2023 Jan 25. doi: 10.1002/jnr.25169. Online ahead of print.


Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) appears to be effective against seizures in animals and humans however, its therapeutic mechanisms remain elusive. This study aimed to combine 9.4T multimodal magnetic resonance imaging (MRI) with histology to investigate the longitudinal effects of long-term ANT-DBS in pilocarpine-induced epileptic rats. Status epilepsy (SE) was induced by LiCl-pilocarpine injection in 11 adult male Sprague-Dawley rats. Four weeks after SE, chronic epileptic rats underwent either ANT-DBS (n = 6) or sham-DBS (n = 5) surgery. Electroencephalography (EEG) and spontaneous recurrent seizures (SRS) were recorded for 1 week. The T2-weighted image and images from resting-state functional MRI (rs-fMRI) were acquired at three states: before SE, at 4 weeks post-SE, and at 5 weeks post-DBS. Volumes of the hippocampal subregions and hippocampal-related functional connectivity (FC) were compared longitudinally. Finally, antibodies against neuronal nuclei (NeuN) and glial fibrillary acidic proteins were used to evaluate neuronal loss and astrogliosis in the hippocampus. Long-term ANT-DBS significantly reduced seizure generalization in pilocarpine-induced epileptic rats. By analyzing the gray matter volume using T2-weighted images, long-term ANT-DBS displayed morphometric restoration of the hippocampal subregions. Neuronal protection of the hippocampal subregions and inhibition of astrogliosis in the hippocampal subregions were observed in the ANT-DBS group. ANT-DBS caused reversible regulation of FC in the insula-hippocampus and subthalamic nucleus-hippocampus. Long-term ANT-DBS provides comprehensive protection of hippocampal histology, hippocampal morphometrics, and hippocampal-related functional networks.

PMID:36696411 | DOI:10.1002/jnr.25169

Intrinsic hippocampal connectivity is associated with individual differences in retrospective duration processing

Wed, 01/25/2023 - 11:00

Brain Struct Funct. 2023 Jan 25. doi: 10.1007/s00429-023-02612-3. Online ahead of print.


The estimation of incidentally encoded durations of time intervals (retrospective duration processing) is thought to rely on the retrieval of contextual information associated with a sequence of events, automatically encoded in medial temporal lobe regions. "Time cells" have been described in the hippocampus (HC), encoding the temporal progression of events and their duration. However, whether the HC supports explicit retrospective duration judgments in humans, and which neural dynamics are involved, is still poorly understood. Here we used resting-state fMRI to test the relation between variations in intrinsic connectivity patterns of the HC, and individual differences in retrospective duration processing, assessed using a novel task involving the presentation of ecological stimuli. Results showed that retrospective duration discrimination performance predicted variations in the intrinsic connectivity of the bilateral HC with the right precentral gyrus; follow-up exploratory analyses suggested a role of the CA1 and CA4/DG subfields in driving the observed pattern. Findings provide insights on neural networks associated with implicit processing of durations in the second range.

PMID:36695891 | DOI:10.1007/s00429-023-02612-3

EEG cortical activity and connectivity correlates of early sympathetic response during cold pressor test

Tue, 01/24/2023 - 11:00

Sci Rep. 2023 Jan 24;13(1):1338. doi: 10.1038/s41598-023-27480-z.


Previous studies have identified several brain regions involved in the sympathetic response and its integration with pain, cognition, emotions and memory processes. However, little is known about how such regions dynamically interact during a sympathetic activation task. In this study, we analyzed EEG activity and effective connectivity during a cold pressor test (CPT). A source localization analysis identified a network of common active sources including the right precuneus (r-PCu), right and left precentral gyri (r-PCG, l-PCG), left premotor cortex (l-PMC) and left anterior cingulate cortex (l-ACC). We comprehensively analyzed the network dynamics by estimating power variation and causal interactions among the network regions through the direct directed transfer function (dDTF). A connectivity pattern dominated by interactions in [Formula: see text] (8-12) Hz band was observed in the resting state, with r-PCu acting as the main hub of information flow. After the CPT onset, we observed an abrupt suppression of such [Formula: see text]-band interactions, followed by a partial recovery towards the end of the task. On the other hand, an increase of [Formula: see text]-band (1-4) Hz interactions characterized the first part of CPT task. These results provide novel information on the brain dynamics induced by sympathetic stimuli. Our findings suggest that the observed suppression of [Formula: see text] and rise of [Formula: see text] dynamical interactions could reflect non-pain-specific arousal and attention-related response linked to stimulus' salience.

PMID:36693870 | PMC:PMC9873641 | DOI:10.1038/s41598-023-27480-z

Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network

Tue, 01/24/2023 - 11:00

Elife. 2023 Jan 24;12:e78397. doi: 10.7554/eLife.78397.


Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.

PMID:36693116 | PMC:PMC9873253 | DOI:10.7554/eLife.78397

Hemodynamic transient and functional connectivity follow structural connectivity and cell type over the brain hierarchy

Tue, 01/24/2023 - 11:00

Proc Natl Acad Sci U S A. 2023 Jan 31;120(5):e2202435120. doi: 10.1073/pnas.2202435120. Epub 2023 Jan 24.


The neural circuit of the brain is organized as a hierarchy of functional units with wide-ranging connections that support information flow and functional connectivity. Studies using MRI indicate a moderate coupling between structural and functional connectivity at the system level. However, how do connections of different directions (feedforward and feedback) and regions with different excitatory and inhibitory (E/I) neurons shape the hemodynamic activity and functional connectivity over the hierarchy are unknown. Here, we used functional MRI to detect optogenetic-evoked and resting-state activities over a somatosensory pathway in the mouse brain in relation to axonal projection and E/I distribution. Using a highly sensitive ultrafast imaging, we identified extensive activation in regions up to the third order of axonal projections following optogenetic excitation of the ventral posteriomedial nucleus of the thalamus. The evoked response and functional connectivity correlated with feedforward projections more than feedback projections and weakened with the hierarchy. The hemodynamic response exhibited regional and hierarchical differences, with slower and more variable responses in high-order areas and bipolar response predominantly in the contralateral cortex. Electrophysiological recordings suggest that these reflect differences in neural activity rather than neurovascular coupling. Importantly, the positive and negative parts of the hemodynamic response correlated with E/I neuronal densities, respectively. Furthermore, resting-state functional connectivity was more associated with E/I distribution, whereas stimulus-evoked effective connectivity followed structural wiring. These findings indicate that the structure-function relationship is projection-, cell-type- and hierarchy-dependent. Hemodynamic transients could reflect E/I activity and the increased complexity of hierarchical processing.

PMID:36693103 | DOI:10.1073/pnas.2202435120

Examining the usefulness of the brain network marker program using fMRI for the diagnosis and stratification of major depressive disorder: a non-randomized study protocol

Tue, 01/24/2023 - 11:00

BMC Psychiatry. 2023 Jan 24;23(1):63. doi: 10.1186/s12888-023-04560-y.


BACKGROUND: Although many studies have reported the biological basis of major depressive disorder (MDD), none have been put into practical use. Recently, we developed a generalizable brain network marker for MDD diagnoses (diagnostic marker) across multiple imaging sites using resting-state functional magnetic resonance imaging (rs-fMRI). We have planned this clinical trial to establish evidence for the practical applicability of this diagnostic marker as a medical device. In addition, we have developed generalizable brain network markers for MDD stratification (stratification markers), and the verification of these brain network markers is a secondary endpoint of this study.

METHODS: This is a non-randomized, open-label study involving patients with MDD and healthy controls (HCs). We will prospectively acquire rs-fMRI data from 50 patients with MDD and 50 HCs and anterogradely verify whether our diagnostic marker can distinguish between patients with MDD and HCs. Furthermore, we will longitudinally obtain rs-fMRI and clinical data at baseline and 6 weeks later in 80 patients with MDD treated with escitalopram and verify whether it is possible to prospectively distinguish MDD subtypes that are expected to be effectively responsive to escitalopram using our stratification markers.

DISCUSSION: In this study, we will confirm that sufficient accuracy of the diagnostic marker could be reproduced for data from a prospective clinical study. Using longitudinally obtained data, we will also examine whether the "brain network marker for MDD diagnosis" reflects treatment effects in patients with MDD and whether treatment effects can be predicted by "brain network markers for MDD stratification". Data collected in this study will be extremely important for the clinical application of the brain network markers for MDD diagnosis and stratification.

TRIAL REGISTRATION: Japan Registry of Clinical Trials ( jRCTs062220063 ). Registered 12/10/2022.

PMID:36694153 | DOI:10.1186/s12888-023-04560-y

Altered brain activity and functional connectivity in migraine without aura during and outside attack

Tue, 01/24/2023 - 11:00

Neurol Res. 2023 Jan 24:1-7. doi: 10.1080/01616412.2023.2170938. Online ahead of print.


BACKGROUND: Migraine is commonly seen as a cyclic disorder with variable cortical excitability at different phases. Herein, we investigated the cortical excitability in migraine without aura patients during an attack (MWoA-DA) and interictal period (MWoA-DI) and further explored the functional connectivity (FC) in brain regions with cortical excitability abnormalities in patients.

METHODS: Seven MWoA-DA patients, twenty-seven MWoA-DI patients, and twenty-nine healthy controls (HC) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scan. The amplitude of low-frequency fluctuations (ALFF) was assessed to identify spontaneous brain activity. Then, brain regions showing significant differences across groups were identified as regions of interest (ROI) in FC analysis.

RESULTS: Compared with MWoA-DI patients and HC, the ALFF in the trigeminocervical complex (TCC) was higher in the MWoA-DA patients. Decreased FC in MWoA-DA patients was found between TCC and left postcentral gyrus compared with MWoA-DI patients. Compared with HC, ALFF was lower in the right cuneus but higher in the right rolandic operculum of MWoA-DI patients. Additionally, the ALFF in the right cuneus was negatively correlated with the Migraine Disability Assessment Scale (MIDAS) in MWoA-DI patients.

CONCLUSIONS: The trigeminovascular system and impairments in descending pain modulatory pathways participate in the pathophysiology of migraine during the ictal period. The defense effect exists in the interictal phase, and the dysfunction in the cuneus may be related to the disease severity. This dynamic change in different brain regions could deepen our understanding of the physiopathology underlying migraine.

PMID:36693797 | DOI:10.1080/01616412.2023.2170938

A study on alterations in functional activity in migraineurs during the interictal period

Tue, 01/24/2023 - 11:00

Heliyon. 2022 Dec 16;9(1):e12372. doi: 10.1016/j.heliyon.2022.e12372. eCollection 2023 Jan.


Migraine is a recurrent disease in which the cumulative effect of repeated pain attacks over a long period of time causes changes in brain function. Although there are some studies focusing on the interictal period of migraine, the reproducibility of these results is poor. Therefore, we intend to use a data-driven functional connectivity (FC) approach to probe the alterations in cerebral functional activity during the interictal period, as well as underlying no-task mechanisms of inducing headache attack in migraine patients. In the current research, 24 episodic migraine patients and 23 healthy controls (HCs) were recruited. By analyzing the magnitude of regional homogeneity (ReHo) and low-frequency fractional fluctuation (fALFF), We identified alterations in spontaneous brain activity in migraineurs, including the bilateral middle frontal gyrus, left postcentral, and right lingual gyrus. Thereafter such abnormalities were selected as seeds (ROIs) for FC analysis to further explore the underlying changes between ROIs and the whole brain areas. Compared with HCs, FC between the right middle frontal gyrus with the left precuneus cortex, and bilateral thalamus were enhanced in migraineurs. In addition, increased FC has been showed between the left postcentral gyrus with the bilateral thalamus. Furthermore, negative correlation existed between fALFF values of the left middle frontal gyrus and the pain intensity of migraine attacks (r = -0.4578, p = 0.0245). In summary, abnormal FC between the bilateral thalamus and right middle frontal gyrus, or the left retrocentral gyrus may occur between attacks in migraineurs, which may be the basis for sensory integration and pain regulation dysfunction. Thus, this could become a promising biomarker for the early diagnosis and evaluation of migraine in the interictal period, and provide a novel view for further investigation of the pathogenesis and etiology of recurrent migraine.

PMID:36691529 | PMC:PMC9860458 | DOI:10.1016/j.heliyon.2022.e12372

The use of chemogenetic actuator ligands in nonhuman primate DREADDs-fMRI

Tue, 01/24/2023 - 11:00

Curr Res Neurobiol. 2022 Dec 30;4:100072. doi: 10.1016/j.crneur.2022.100072. eCollection 2023.


Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are engineered receptors that allow for genetically targeted, reversible manipulation of cellular activity via systemic drug administration. DREADD induced manipulations are initiated via the binding of an actuator ligand. Therefore, the use of DREADDs is contingent on the availability of actuator ligands. Actuator ligands low-dose clozapine (CLZ) and deschloroclozapine (DCZ) are highly selective for DREADDs, and, upon binding, induce physiological and behavioral changes in rodents and nonhuman primates (NHPs). Despite this reported specificity, both CLZ and DCZ have partial affinity for a variety of endogenous receptors and can induce dose-specific changes even in naïve animals. As such, this study aimed to examine the effects of CLZ and DCZ on resting-state functional connectivity (rs-FC) and intrinsic neural timescales (INTs) in naïve NHPs. In doing so, we evaluated whether CLZ and DCZ - in the absence of DREADDs - are inert by examining these ligands' effects on the intrinsic functional properties of the brain. Low-dose DCZ did not induce consistent changes in rs-FC or INTs prior to the expression of DREADDs; however, a high dose resulted in subject-specific changes in rs-FC and INTs. In contrast, CLZ administration induced consistent changes in rs-FC and INTs prior to DREADD expression in our subjects. Our results caution against the use of CLZ by explicitly demonstrating the impact of off-target effects that can confound experimental results. Altogether, these data endorse the use of low dose DCZ for future DREADD-based experiments.

PMID:36691404 | PMC:PMC9860110 | DOI:10.1016/j.crneur.2022.100072

A Measure of Neural Function Provides Unique Insights into Behavioral Deficits in Acute Stroke

Mon, 01/23/2023 - 11:00

Stroke. 2023 Feb;54(2):e25-e29. doi: 10.1161/STROKEAHA.122.040841. Epub 2023 Jan 23.


BACKGROUND: Clinical and neuroimaging measures incompletely explain behavioral deficits in the acute stroke setting. We hypothesized that electroencephalography (EEG)-based measures of neural function would significantly improve prediction of acute stroke deficits.

METHODS: Patients with acute stroke (n=50) seen in the emergency department of a university hospital from 2017 to 2018 underwent standard evaluation followed by a 3-minute recording of EEG at rest using a wireless, 17-electrode, dry-lead system. Artifacts in EEG recordings were removed offline and then spectral power was calculated for each lead pair. A primary EEG metric was DTABR, which is calculated as a ratio of spectral power: [(Delta*Theta)/(Alpha*Beta)]. Bivariate analyses and least absolute shrinkage and selection operator (LASSO) regression identified clinical and neuroimaging measures that best predicted initial National Institutes of Health Stroke Scale (NIHSS) score. Multivariable linear regression was then performed before versus after adding EEG findings to these measures, using initial NIHSS score as the dependent measure.

RESULTS: Age, diabetes status, and infarct volume were the best predictors of initial NIHSS score in bivariate analyses, confirmed using LASSO regression. Combined in a multivariate model, these 3 explained initial NIHSS score (adjusted r2=0.47). Adding any of several different EEG measures to this clinical model significantly improved prediction; the greatest amount of additional variance was explained by adding contralesional DTABR (adjusted r2=0.60, P<0.001).

CONCLUSIONS: EEG measures of neural function significantly add to clinical and neuroimaging for explaining initial NIHSS score in the acute stroke emergency department setting. A dry-lead EEG system can be rapidly and easily implemented. EEG contains information that may be useful early after stroke.

PMID:36689596 | PMC:PMC9881885 | DOI:10.1161/STROKEAHA.122.040841

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Mon, 01/23/2023 - 11:00

BMC Psychiatry. 2023 Jan 23;23(1):59. doi: 10.1186/s12888-022-04509-7.


BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.

METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.

RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.

CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

PMID:36690972 | DOI:10.1186/s12888-022-04509-7

Alterations in regional homogeneity and functional connectivity associated with cognitive impairment in patients with hypertension: a resting-state functional magnetic resonance imaging study

Mon, 01/23/2023 - 11:00

Hypertens Res. 2023 Jan 23. doi: 10.1038/s41440-023-01168-3. Online ahead of print.


Our study aims to investigate the alterations and diagnostic efficiency of regional homogeneity (ReHo) and functional connectivity (FC) in hypertension patients with cognitive impairment. A total of 62 hypertension patients with cognitive impairment (HTN-CI), 59 hypertension patients with normal cognition (HTN-NC), and 58 healthy controls (HCs) with rs-fMRI data were enrolled in this study. Univariate analysis (based on whole-brain ReHo and seed-based FC maps) was performed to observe brain regions with significant differences among the three groups. Multiple voxel pattern analysis (MVPA) was applied to evaluate the diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. Compared with the HCs and HTN-NC, HTN-CI exhibited decreased ReHo in the right caudate, left postcentral gyrus, posterior cingulate gyrus, insula, while increased ReHo in the left superior occipital gyrus and superior parietal gyrus. HTN-CI showed increased FC between seed regions (left posterior cingulate gyrus, insula, postcentral gyrus) with many specific brain regions. MVPA analysis (based on whole-brain ReHo and seed-based FC maps) displayed high classification ability in distinguishing HTN-CI from HTN-NC and HCs. The ReHo values (right caudate) and the FC values (left postcentral gyrus seed to left posterior cingulate gyrus) were positively correlated with the MoCA scores in HTN-CI. HTN-CI was associated with decreased ReHo and increased FC mainly in the left posterior cingulate gyrus, postcentral gyrus, insula compared to HTN-NC and HC. Besides, MVPA analysis yields excellent diagnostic accuracy in classifying HTN-CI from HTN-NC and HCs. The findings may contribute to unveiling the underlying neuropathological mechanism of HTN-CI.

PMID:36690806 | DOI:10.1038/s41440-023-01168-3