Most recent paper

Amygdala and Insula Connectivity Changes Following Psychotherapy for Posttraumatic Stress Disorder: A Randomized Clinical Trial.

Biol Psychiatry. 2020 Dec 08;:

Authors: Fonzo GA, Goodkind MS, Oathes DJ, Zaiko YV, Harvey M, Peng KK, Weiss ME, Thompson AL, Zack SE, Lindley SE, Arnow BA, Jo B, Rothbaum BO, Etkin A

Abstract
BACKGROUND: Exposure-based psychotherapy is a first-line treatment for posttraumatic stress disorder (PTSD), but its mechanisms are poorly understood. Functional brain connectivity is a promising metric for identifying treatment mechanisms and biosignatures of therapeutic response. To this end, we assessed amygdala and insula treatment-related connectivity changes and their relationship to PTSD symptom improvements.
METHODS: Individuals with a primary PTSD diagnosis (N = 66) participated in a randomized clinical trial of prolonged exposure therapy (n = 36) versus treatment waiting list (n = 30). Task-free functional magnetic resonance imaging was completed prior to randomization and 1 month following cessation of treatment/waiting list. Whole-brain blood oxygenation level-dependent responses were acquired. Intrinsic connectivity was assessed by subregion in the amygdala and insula, limbic structures key to the disorder pathophysiology. Dynamic causal modeling assessed evidence for effective connectivity changes in select nodes informed by intrinsic connectivity findings.
RESULTS: The amygdala and insula displayed widespread patterns of primarily subregion-uniform intrinsic connectivity change, including increased connectivity between the amygdala and insula; increased connectivity of both regions with the ventral prefrontal cortex and frontopolar and sensory cortices; and decreased connectivity of both regions with the left frontoparietal nodes of the executive control network. Larger decreases in amygdala-frontal connectivity and insula-parietal connectivity were associated with larger PTSD symptom reductions. Dynamic causal modeling evidence suggested that treatment decreased left frontal inhibition of the left amygdala, and larger decreases were associated with larger symptom reductions.
CONCLUSIONS: PTSD psychotherapy adaptively attenuates functional interactions between frontoparietal and limbic brain circuitry at rest, which may reflect a potential mechanism or biosignature of recovery.

PMID: 33516458 [PubMed - as supplied by publisher]

Brain activity alterations in patients with Parkinson's disease with cognitive impairment based on resting-state functional MRI.

Neurosci Lett. 2021 Jan 27;:135672

Authors: Guo W, Jin W, Li N, Gao J, Wang J, Chang Y, Yin K, Chen Y, Zhang S, Wang T

Abstract
OBJECTIVE: This study aimed to investigate the differences in regional homogeneity (ReHo) values in patients with Parkinson's disease (PD) with cognitive impairment (PD-CI) and thus explore the neuropathological mechanism of PD-CI.
METHODS: Resting-state functional magnetic resonance imaging scans were obtained from 36 patients with PD and 20 healthy controls (HCs) in this study. The PD group comprised 20 patients with PD-CI and 16 patients with PD with normal cognitive function (PD-NC). The data were analyzed using ReHo analysis to observe the changes in brain activity in patients with PD-CI and PD-NC. Statistical comparison was performed using covariance analysis and post hoc t tests.
RESULTS: The patients in the PD-CI group were older than those in the PD-NC and HC groups. Compared with the HC group, the PD-CI group showed that the ReHo value decreased in the right supplementary motor area, left lingual gyrus, left thalamus, and left precuneus, but increased in the left fusiform gyrus. Compared with the HC group, the PD-NC group showed that the ReHo value decreased in the right cerebellum_6, but increased in the left inferior temporal gyrus, left orbital inferior frontal gyrus, and left precentral gyrus. Compared with the PD-NC group, the PD-CI group showed that the ReHo value decreased in the right precuneus, left triangular inferior frontal gyrus, left middle frontal gyrus, right opercular inferior frontal gyrus, left orbital inferior frontal gyrus, left supramarginal gyrus, left angular gyrus, left inferior temporal gyrus, and right cerebelum_7b, but increased in the left precentral gyrus and left fusiform gyrus.
CONCLUSIONS: Age was a risk factor for cognitive decline in patients with PD. The ReHo value in the default mode network (DMN) was closely related to PD cognitive function, and the DMN was affected before CI and continuously deteriorated with disease progression. The disorder of visual conduction pathway was involved in CI in patients with PD, but these patients could recruit cognitive resources by improving visual-spatial ability. The cognitive function in such patients was related to the dopaminergic, cholinergic, and noradrenergic systems. The information transmission efficiency of the cerebellum-thalamus-cortex loop was reduced and involved in the cognitive decline process in patients with PD.

PMID: 33515623 [PubMed - as supplied by publisher]

Aberrant dynamic functional connectivity of default mode network predicts symptom severity in major depressive disorder.

Brain Connect. 2021 Jan 29;:

Authors: Sendi MSE, Zendehrouh E, Sui J, Fu Z, Zhi D, Lv L, Ma X, Ke Q, Li X, Wang C, Abbott C, Turner JA, Miller RL, Calhoun V

Abstract
BACKGROUND: Major depressive disorder (MDD) is a complex mental disorder characterized by a persistent sad feeling and lack of interest. The default mode network (DMN) is a set of brain areas that is more active during rest and deactivate during a goal-oriented behavior. Recent studies have shown abnormal static functional connectivity in the DMN of MDD. In this work, we extend previous findings by evaluating dynamic functional connectivity (dFC) within the DMN subnodes in MDD.
METHODS: We analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data of 262 MDD patients and 277 healthy controls (HCs). We employed a sliding-window approach to estimate dFCs for seven subnodes of the DMN, including anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), followed by clustering the dFCs into five recurring brain states. Classification of MDD and HC subjects based on within-state FC was performed using a logistic regression classifier with elastic net regularization. Transition probabilities between dFC states were used to identify relationships between symptom severity and dFC data in MDD patients.
RESULTS: By comparing state-specific FC between HC and MDD, a disrupted connectivity pattern was observed within DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD subjects showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This study is the first attempt to study dFC of the DMN in MDD using a relatively large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects.

PMID: 33514278 [PubMed - as supplied by publisher]

Specific Brain Reorganization Underlying Superior Upper Limb Motor Function After Spinal Cord Injury: A Multimodal MRI Study.

Neurorehabil Neural Repair. 2021 Jan 29;:1545968321989347

Authors: Nakanishi T, Nakagawa K, Kobayashi H, Kudo K, Nakazawa K

Abstract
BACKGROUND: We recently discovered that individuals with complete spinal cord injury (SCI) have a higher grip force control ability in their intact upper limbs than able-bodied subjects. However, the neural basis for this phenomenon is unknown.
OBJECTIVE: This study aimed to investigate the neural basis of the higher grip force control in the brains of individuals with SCI using multimodal magnetic resonance imaging (MRI).
METHODS: Eight SCI subjects and 10 able-bodied subjects performed hand grip force control tasks at 10%, 20%, and 30% of their maximal voluntary contraction during functional MRI (fMRI). Resting-state fMRI and T1-weighted structural images were obtained to investigate changes in brain networks and structures after SCI.
RESULTS: SCI subjects showed higher grip force steadiness than able-bodied subjects (P < .05, corrected), smaller activation in the primary motor cortex (P < .05, corrected), and deactivation of the visual cortex (P < .001, uncorrected). Furthermore, SCI subjects had stronger functional connectivity between the superior parietal lobule and the left primary motor cortex (P < .001, uncorrected), as well as larger gray matter volume in the bilateral superior parietal lobule (P < .001, uncorrected).
CONCLUSIONS: The structural and functional reorganization observed in the superior parietal lobule of SCI subjects may represent the neural basis underlying the observed higher grip force control, and is likely responsible for the smaller activation in the primary motor cortex observed in these individuals. These findings could have applications in the fields of neurorehabilitation for improvement of intact limb functions after SCI.

PMID: 33514276 [PubMed - as supplied by publisher]

High spatial correlation in brain connectivity between micturition and resting states within bladder-related networks using 7 T MRI in multiple sclerosis women with voiding dysfunction.

World J Urol. 2021 Jan 29;:

Authors: Shi Z, Tran K, Karmonik C, Boone T, Khavari R

Abstract
BACKGROUND: Several studies have reported brain activations and functional connectivity (FC) during micturition using functional magnetic resonance imaging (fMRI) and concurrent urodynamics (UDS) testing. However, due to the invasive nature of UDS procedure, non-invasive resting-state fMRI is being explored as a potential alternative. The purpose of this study is to evaluate the feasibility of utilizing resting states as a non-invasive alternative for investigating the bladder-related networks in the brain.
METHODS: We quantitatively compared FC in brain regions belonging to the bladder-related network during the following states: 'strong desire to void', 'voiding initiation (or attempt at voiding initiation)', and 'voiding (or continued attempt of voiding)' with FC during rest in nine multiple sclerosis women with voiding dysfunction using fMRI data acquired at 7 T and 3 T.
RESULTS: The inter-subject correlation analysis showed that voiding (or continued attempt of voiding) is achieved through similar network connections in all subjects. The task-based bladder-related network closely resembles the resting-state intrinsic network only during voiding (or continued attempt of voiding) process but not at other states.
CONCLUSION: Resting states fMRI can be potentially utilized to accurately reflect the voiding (or continued attempt of voiding) network. Concurrent UDS testing is still necessary for studying the effects of strong desire to void and initiation of voiding (or attempt at initiation of voiding).

PMID: 33512570 [PubMed - as supplied by publisher]

Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment.

Neural Plast. 2021;2021:8846097

Authors: Bezmaternykh DD, Melnikov MY, Savelov AA, Kozlova LI, Petrovskiy ED, Natarova KA, Shtark MB

Abstract
Network mechanisms of depression development and especially of improvement from nonpharmacological treatment remain understudied. The current study is aimed at examining brain networks functional connectivity in depressed patients and its dynamics in nonpharmacological treatment. Resting state fMRI data of 21 healthy adults and 51 patients with mild or moderate depression were analyzed with spatial independent component analysis; then, correlations between time series of the components were calculated and compared between-group (study 1). Baseline and repeated-measure data of 14 treated (psychotherapy or fMRI neurofeedback) and 15 untreated depressed participants were similarly analyzed and correlated with changes in depression scores (study 2). Aside from diverse findings, studies 1 and 2 both revealed changes in within-default mode network (DMN) and DMN to executive control network (ECN) connections. Connectivity in one pair, initially lower in depression, decreased in no treatment group and was inversely correlated with Montgomery-Asberg depression score change in treatment group. Weak baseline connectivity in this pair also predicted improvement on Montgomery-Asberg scale in both treatment and no treatment groups. Coupling of another pair, initially stronger in depression, increased in therapy though was unrelated to improvement. The results demonstrate possible role of within-DMN and DMN-ECN functional connectivity in depression treatment and suggest that neural mechanisms of nonpharmacological treatment action may be unrelated to normalization of initially disrupted connectivity.

PMID: 33510782 [PubMed - in process]

The Relationship of Functional Connectivity of the Sensorimotor and Visual Cortical Networks Between Resting and Task States.

Front Neurosci. 2020;14:592720

Authors: Xiong Z, Tian C, Zeng X, Huang J, Wang R

Abstract
The intrinsic activity of the human brain maintains its general operation at rest, and this ongoing spontaneous activity exhibits a high level of spatiotemporally correlated activity among different cortical areas, showing intrinsically organized brain functional connectivity (FC) networks. Many functional network properties of the human brain have been investigated extensively for both rest and task states, but the relationship between these two states has been rarely investigated yet and remains unclear. Comparing well-defined task-specific networks with corresponding intrinsic FC networks may reveal their relationship and improve our understanding of the brain's operations at both rest and task states. This study investigated the relationship of the sensorimotor and visual cortical FC networks between the resting and task states. The sensorimotor task was to rub right-hand fingers, and the visual task was to open and close eyes, respectively. Our study demonstrated a general relationship of the task-evoked FC network with its corresponding intrinsic FC network, regardless of the tasks. For each task type, the study showed that (1) the intrinsic and task-evoked FC networks shared a common network and the task enhanced the coactivity within that common network compared to the intrinsic activity; (2) some areas within the intrinsic FC network were not activated by the task, and therefore, the task activated only partial but not whole of the intrinsic FC network; and (3) the task activated substantial additional areas outside the intrinsic FC network and therefore recruited more intrinsic FC networks to perform the task.

PMID: 33510609 [PubMed]

Measuring inter-individual differences in stress sensitivity during MR-guided prostate biopsy.

Sci Rep. 2021 Jan 28;11(1):2454

Authors: Kohn N, Heidkamp J, Fernández G, Fütterer J, Tendolkar I

Abstract
People often experience high level of distress during invasive interventions, which may exceed their coping abilities. This may be in particular evident when confronted with the suspicion of cancer. Taking the example of prostate biopsy sampling, we aimed at investigating the impact of an MRI guided prostate biopsy on the acute stress response and its mechanistic basis. We recruited 20 men with a clinical suspicion of prostate cancer. Immediately before an MRI guided biopsy procedure, we conducted fMRI in the same scanner to assess resting-state brain connectivity. Physiological and hormonal stress measures were taken during the procedure and associated with questionnaires, hair cortisol levels and brain measures to elucidate mechanistic factors for elevated stress. As expected, patients reported a stress-related change in affect. Decreased positive affect was associated with higher hair but not saliva cortisol concentration. Stronger use of maladaptive emotion regulation techniques, elevated depression scores and higher within-salience-network connectivity was associated with stronger increase in negative affect and/or decrease of positive affect during the procedure. While being limited in its generalization due to age, sample size and gender, our proof of concept study demonstrates the utility of real-life stressors and large-scale brain network measures in stress regulation research with potential impact in clinical practice.

PMID: 33510379 [PubMed - in process]

Use of common average reference and large-Laplacian spatial-filters enhances EEG signal-to-noise ratios in intrinsic sensorimotor activity.

J Neurosci Methods. 2021 Jan 25;:109089

Authors: Tsuchimoto S, Shibusawa S, Iwama S, Hayashi M, Okuyama K, Mizuguchi N, Kato K, Ushiba J

Abstract
Background Oscillations in the resting-state scalp electroencephalogram (EEG) represent various intrinsic brain activities. One of the characteristic EEG oscillations is the sensorimotor rhythm (SMR)-with its arch-shaped waveform in alpha- and betabands-that reflect sensorimotor activity. The representation of sensorimotor activity by the SMR depends on the signal-to-noise ratio achieved by EEG spatial filters. New method We employed simultaneous recording of EEG and functional magnetic resonance imaging, and 10-min resting-state brain activities were recorded in 19 healthy volunteers. To compare the EEG spatial-filtering methods commonly used for extracting sensorimotor cortical activities, we assessed nine different spatial-filters: a default reference of EEG amplifier system, a common average reference (CAR), small-, middle- and large-Laplacian filters, and four types of bipolar manners (C3-Cz, C3-F3, C3-P3, and C3-T7). We identified the brain region that correlated with the EEG-SMR power obtained after each spatial-filtering method was applied. Subsequently, we calculated the proportion of the significant voxels in the sensorimotor cortex as well as the sensorimotor occupancy in all significant regions to examine the sensitivity and specificity of each spatial-filter. Results The CAR and large-Laplacian spatial-filters were superior at improving the signal-to-noise ratios for extracting sensorimotor activity from the EEG-SMR signal. Comparison with existing methods Our results are consistent with the spatial-filter selection to extract task-dependent activation for better control of EEG-SMR-based interventions. Our approach has the potential to identify the optimal spatial-filter for EEG-SMR. Conclusions Evaluating spatial-filters for extracting spontaneous sensorimotor activity from the EEG is a useful procedure for constructing more effective EEG-SMR-based interventions.

PMID: 33508408 [PubMed - as supplied by publisher]

Aging effect on head motion: A Machine Learning study on resting state fMRI data.

J Neurosci Methods. 2021 Jan 25;:109084

Authors: Saccà V, Sarica A, Quattrone A, Rocca F, Quattrone A, Novellino F

Abstract
BACKGROUND: Resting-state-fMRI is a technique used to explore the functional brain architecture in term of brain networks and their interactions. However, the robustness of Resting-state-fMRI analysis is negatively affected by physiological noise caused by subject head motion. The aim of our study was to provide new knowledge about the effect of normal aging on the head motion signals.
NEW METHOD: For the first time, we proposed a method for evaluating the most sensitive head motion parameters linked to subjects'aging. We enrolled 14-young(9females; mean-age = 28 ± 4.07) and 14-elderly(9females; mean-age = 66 ± 5.19) subjects. Along three axes(X,Y,Z), we extracted six motions parameters which reflected the head's movements to characterize translations(x,y,z) and rotations(angles phi,theta,psi). We performed:1)univariate analysis for comparing the groups and correlation to investigate the relationship between age and movement parameters; 2)Support-Vector-Machine, using bootstrap and calculating the feature importance.
RESULTS: Statistical analyses showed significant association between the aging and some motion's parameters(rotation psi; translations y and z). These results were also confirmed by multivariate analysis with Support-Vector-Machine that presented an AUC of 90%.
COMPARISON TO EXISTING METHODS: The proposed method shows that normal aging produces significant increase in head motion parameters, highlighting the critical effect of motion on resting data analyses in particular considering psi, y and z movements. To our knowledge and at the present, this represents the first study investigating the accurate characterization of motion parameters in aging.
CONCLUSIONS: Our results have a high impact to improve healthy control recruitment and appropriately decreasing the risk of signal distortion, according to the age of enrolled subjects.

PMID: 33508406 [PubMed - as supplied by publisher]

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome.

PLoS Comput Biol. 2021 Jan 28;17(1):e1008310

Authors: Aqil M, Atasoy S, Kringelbach ML, Hindriks R

Abstract
Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed "connectome harmonics", have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

PMID: 33507899 [PubMed - as supplied by publisher]

Association Between Greater Cerebellar Network Connectivity and Improved Phonemic Fluency Performance After Exercise Training in Older Adults.

Cerebellum. 2021 Jan 28;:

Authors: Won J, Faroqi-Shah Y, Callow DD, Williams A, Awoyemi A, Nielson KA, Smith JC

Abstract
Little is known about the effects of exercise training (ET) on lexical characteristics during fluency task and its association with cerebellum functional connectivity. The purposes of this study were (1) to investigate whether ET alters response patterns during phonemic and semantic fluency tasks and (2) to assess the association between ET-related changes in cerebellum functional connectivity (FC) and lexical characteristics during fluency tasks. Thirty-five older adults (78.0 ± 7.1 years; 17 mild cognitive impairment (MCI) and 18 healthy cognition (HC)) underwent a 12-week treadmill ET. Before and after ET, cardiorespiratory fitness tests, phonemic and semantic fluency tests, and resting-state fMRI scans were administered. We utilized a seed-based correlation analysis to measure cerebellum FC and linear regression to assess the association of residualized ET-induced Δcerebellum FC with Δtask performance. Improved mean switches and frequency during the phonemic fluency task were observed following ET in all participants. There were significant associations between ET-induced increases in cerebellum FC and greater phonemic fluency task log frequency, increases in mean switches, and a reduction in the number of syllables in HC. Lastly, there was a significant interaction between group and cerebellar connectivity on phonemic fluency mean log frequency and number of syllables. A 12-week walking ET is related to enhanced phonemic fluency lexical characteristics in older adults with MCI and HC. The association between ET-induced increases in cerebellum FC and enhanced response patterns after ET suggests that the cerebellum may play an important role in ET-related improvement in phonemic fluency performance in cognitively healthy older adults.

PMID: 33507462 [PubMed - as supplied by publisher]

Voxel-Wise Brain-Wide Functional Connectivity Abnormalities in Patients with Primary Blepharospasm at Rest.

Neural Plast. 2021;2021:6611703

Authors: Pan P, Wei S, Li H, Ou Y, Liu F, Jiang W, Li W, Lei Y, Tang Y, Guo W, Luo S

Abstract
Background: Primary blepharospasm (BSP) is one of the most common focal dystonia and its pathophysiological mechanism remains unclear. An unbiased method was used in patients with BSP at rest to observe voxel-wise brain-wide functional connectivity (FC) changes.
Method: A total of 48 subjects, including 24 untreated patients with BSP and 24 healthy controls, were recruited to undergo functional magnetic resonance imaging (fMRI). The method of global-brain FC (GFC) was adopted to analyze the resting-state fMRI data. We designed the support vector machine (SVM) method to determine whether GFC abnormalities could be utilized to distinguish the patients from the controls.
Results: Relative to healthy controls, patients with BSP showed significantly decreased GFC in the bilateral superior medial prefrontal cortex/anterior cingulate cortex (MPFC/ACC) and increased GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule, right superior frontal gyrus (SFG), and left paracentral lobule/supplement motor area (SMA), which were included in the default mode network (DMN) and sensorimotor network. SVM analysis showed that increased GFC values in the right postcentral gyrus/precentral gyrus/paracentral lobule could discriminate patients from controls with optimal accuracy, specificity, and sensitivity of 83.33%, 83.33%, and 83.33%, respectively.
Conclusion: This study suggested that abnormal GFC in the brain areas associated with sensorimotor network and DMN might underlie the pathophysiology of BSP, which provided a new perspective to understand BSP. GFC in the right postcentral gyrus/precentral gyrus/paracentral lobule might be utilized as a latent biomarker to differentiate patients with BSP from controls.

PMID: 33505457 [PubMed - in process]

Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features.

Front Neurosci. 2020;14:605697

Authors: Bohaterewicz B, Sobczak AM, Podolak I, Wójcik B, Mȩtel D, Chrobak AA, Fa Frowicz M, Siwek M, Dudek D, Marek T

Abstract
Background: Some studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR.
Methods: Fifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine.
Results: All groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures.
Conclusion: Our findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.

PMID: 33505239 [PubMed]

Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment.

Front Neurosci. 2020;14:588684

Authors: Qian H, Qin D, Qi S, Teng Y, Li C, Yao Y, Wu J

Abstract
Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L1-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

PMID: 33505236 [PubMed]

[Community-informed connectomics of cortical intrinsic organization in subjective cognitive decline].

Zhonghua Nei Ke Za Zhi. 2021 Feb 01;60(2):122-127

Authors: Chen Q, Lu JM, Qing Z, Chen WQ, Sun Y, Li X, Yan X, Li M, Zhang X, Zhang B, Wang ZG

Abstract
Objective: To investigate the intrinsic organization of cortical circuitry in individuals with subjective cognitive decline (SCD) via resting-state functional magnetic resonance imaging (rs-fMRI) connectome analysis and its correlation with cognitive level. Methods: From June 2017 to November 2019, thirty-six middle-aged and elderly individuals with complaints of memory decline and 32 normal controls (NC) were enrolled from communities in Nanjing. We collected cognitive scale performance,T1-weighted imaging (T1WI) and rs-fMRI data of all subjects. There were 5 males and 31 females in the SCD group, with an average age of (64±5) years. In the NC group, there were 8 males and 24 females, with an average age of (65±5) years. Preprocessing of rs-fMRI data was conducted, then the cerebral cortex was divided into 333 cortical parcels (nodes) and 10 predefined communities according to the prior template. Further, we established full connection matrices between cortical parcels and calculated the within-module degree (WMD) and participation coefficient (PC) of each node based on the matrices. The WMD and PC values were compared between the SCD and NC groups,and their correlations with cognitive scale performance were analyzed. Results: Compared to the NC group,the SCD group showed increased WMD in the dorsolateral prefrontal cortex (DLPFC)(P<0.05,FDR corrected) and the middle frontal gyrus (P<0.005,uncorrected) of the right frontoparietal network (FPN). The SCD group also showed decreased WMD(P<0.05,FDR corrected) in the superior occipital gyrus of the left visual network (VN) and decreased PC (P<0.005,uncorrected) in the supramarginal gyrus of the left dorsal attention network (DAN). The WMD values in the DLPFC showed significant positive correlations with the auditory verbal learning test (AVLT)short-delayed memory (r=0.364,P=0.029),recognition memory (r=0.364, P=0.029) and the Boston naming test scores (BNT, r=0.356, P=0.033)in the SCD group. The PC values in the supramarginal gyrus were significantly positively correlated with the BNT scores (r=0.413, P=0.012) in the SCD group. Conclusion: Cortical network imbalance and reconstruction characterized by decreased intra-module connectivity of VN and inter-module connectivity of DAN exist in SCD subjects,while increased intra-module connectivity of FPN may serve in a compensatory way for the early cognitive decline.

PMID: 33503722 [PubMed - as supplied by publisher]

Brain imaging of executive function with the computerised multiple elements test.

Brain Imaging Behav. 2021 Jan 26;:

Authors: Fuentes-Claramonte P, Santo-Angles A, Argila-Plaza I, Lechón M, Guardiola-Ripoll M, Almodóvar-Payá C, Cullen B, Evans JJ, Manly T, Gee A, Maristany T, Sarró S, Pomarol-Clotet E, McKenna PJ, Salvador R

Abstract
The Computerised Multiple Elements Test (CMET) is a novel executive task to assess goal management and maintenance suitable for use within the fMRI environment. Unlike classical executive paradigms, it resembles neuropsychological multi-elements tests that capture goal management in a more ecological way, by requiring the participant to switch between four simple games within a specified time period. The present study aims to evaluate an fMRI version of the CMET and examine its brain correlates. Thirty-one healthy participants performed the task during fMRI scanning. During each block, they were required to play four simple games, with the transition between games being made either voluntarily (executive condition) or automatically (control condition). The executive condition was associated with increased activity in fronto-parietal and cingulo-opercular regions, with anterior insula activity linked to better task performance. In an additional analysis, the activated regions showed to form functional networks during resting-state and to overlap the executive fronto-parietal and cingulo-opercular networks identified in resting-state with independently defined seeds. These results show the ability of the CMET to elicit activity in well-known executive networks, becoming a potential tool for the study of executive impairment in neurological and neuropsychiatric populations in a more ecological way than classical paradigms.

PMID: 33501628 [PubMed - as supplied by publisher]

A NETWORK-BASED APPROACH TO STUDY OF ADHD USING TENSOR DECOMPOSITION OF RESTING STATE FMRI DATA.

Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:544-548

Authors: Li J, Joshi AA, Leahy RM

Abstract
Identifying changes in functional connectivity in Attention Deficit Hyperactivity Disorder (ADHD) using functional magnetic resonance imaging (fMRI) can help us understand the neural substrates of this brain disorder. Many studies of ADHD using resting state fMRI (rs-fMRI) data have been conducted in the past decade with either manually crafted features that do not yield satisfactory performance, or automatically learned features that often lack interpretability. In this work, we present a tensor-based approach to identify brain networks and extract features from rs-fMRI data. Results show the identified networks are interpretable and consistent with our current understanding of ADHD conditions. The extracted features are not only predictive of ADHD score but also discriminative for classification of ADHD subjects from typically developed children.

PMID: 33500749 [PubMed]

Neural correlates of anger expression in patients with PTSD.

Neuropsychopharmacology. 2021 Jan 26;:

Authors: Eshel N, Maron-Katz A, Wu W, Abu-Amara D, Marmar CR, Etkin A

Abstract
Anger is a common and debilitating symptom of post-traumatic stress disorder (PTSD). Although studies have identified brain circuits underlying anger experience and expression in healthy individuals, how these circuits interact with trauma remains unclear. Here, we performed the first study examining the neural correlates of anger in patients with PTSD. Using a data-driven approach with resting-state fMRI, we identified two prefrontal regions whose overall functional connectivity was inversely associated with anger: the left anterior middle frontal gyrus (aMFG) and the right orbitofrontal cortex (OFC). We then used concurrent TMS-EEG to target the left aMFG parcel previously identified through fMRI, measuring its cortical excitability and causal connectivity to downstream areas. We found that low-anger PTSD patients exhibited enhanced excitability in the left aMFG and enhanced causal connectivity between this region and visual areas. Together, our results suggest that left aMFG activity may confer protection against the development of anger, and therefore may be an intriguing target for circuit-based interventions for anger in PTSD.

PMID: 33500557 [PubMed - as supplied by publisher]

Multi-band MEG signatures of BOLD connectivity reorganization during visuospatial attention.

Neuroimage. 2021 Jan 23;:117781

Authors: Favaretto C, Spadone S, Sestieri C, Betti V, Cenedese A, Penna SD, Corbetta M

Abstract
The functional architecture of the resting brain, as measured with the blood oxygenation level-dependent functional connectivity (BOLD-FC), is slightly modified during task performance. In previous work, we reported behaviorally relevant BOLD-FC modulations between visual and dorsal attention regions when subjects performed a visuospatial attention task as compared to central fixation (Spadone et al., 2015). Here we use magnetoencephalography (MEG) in the same group of subjects to identify the electrophysiological correlates of the BOLD-FC modulation found in our previous work. While BOLD-FC topography, separately at rest and during visual attention, corresponded to neuromagnetic Band-Limited Power (BLP) correlation in the alpha and beta bands (8-30 Hz), BOLD-FC modulations evoked by performing the visual attention task (Spadone et al. 2015) did not match any specific oscillatory band BLP modulation. Conversely, following the application of an orthogonal spatial decomposition that identifies common inter-subject co-variations, we found that attention-rest BOLD-FC modulations were recapitulated by multi-spectral BLP-FC components. Notably, individual variability of alpha connectivity between Frontal Eye Fields and visual occipital regions, jointly with decreased interaction in the Visual network, correlated with visual discrimination accuracy. In summary, task-rest BOLD connectivity modulations match multi-spectral MEG BLP connectivity.

PMID: 33497772 [PubMed - as supplied by publisher]