Most recent paper

Front Psychiatry. 2023 Feb 15;14:1068397. doi: 10.3389/fpsyt.2023.1068397. eCollection 2023.

ABSTRACT

INTRODUCTION: The diagnosis of psychiatric disorders is mostly based on the clinical evaluation of the patient's signs and symptoms. Deep learning binary-based classification models have been developed to improve the diagnosis but have not yet reached clinical practice, in part due to the heterogeneity of such disorders. Here, we propose a normative model based on autoencoders.

METHODS: We trained our autoencoder on resting-state functional magnetic resonance imaging (rs-fMRI) data from healthy controls. The model was then tested on schizophrenia (SCZ), bipolar disorder (BD), and attention-deficit hyperactivity disorder (ADHD) patients to estimate how each patient deviated from the norm and associate it with abnormal functional brain networks' (FBNs) connectivity. Rs-fMRI data processing was conducted within the FMRIB Software Library (FSL), which included independent component analysis and dual regression. Pearson's correlation coefficients between the extracted blood oxygen level-dependent (BOLD) time series of all FBNs were calculated, and a correlation matrix was generated for each subject.

RESULTS AND DISCUSSION: We found that the functional connectivity related to the basal ganglia network seems to play an important role in the neuropathology of BD and SCZ, whereas in ADHD, its role is less evident. Moreover, the abnormal connectivity between the basal ganglia network and the language network is more specific to BD. The connectivity between the higher visual network and the right executive control and the connectivity between the anterior salience network and the precuneus networks are the most relevant in SCZ and ADHD, respectively. The results demonstrate that the proposed model could identify functional connectivity patterns that characterize different psychiatric disorders, in agreement with the literature. The abnormal connectivity patterns from the two independent SCZ groups of patients were similar, demonstrating that the presented normative model was also generalizable. However, the group-level differences did not withstand individual-level analysis implying that psychiatric disorders are highly heterogeneous. These findings suggest that a precision-based medical approach, focusing on each patient's specific functional network changes may be more beneficial than the traditional group-based diagnostic classification.

PMID:36873218 | PMC:PMC9975396 | DOI:10.3389/fpsyt.2023.1068397

Front Psychiatry. 2023 Feb 16;14:1084443. doi: 10.3389/fpsyt.2023.1084443. eCollection 2023.

ABSTRACT

BACKGROUND: As one of the most common diseases, major depressive disorder (MDD) has a significant adverse impact on the li of patients. As a mild form of depression, subclinical depression (SD) serves as an indicator of progression to MDD. This study analyzed the degree centrality (DC) for MDD, SD, and healthy control (HC) groups and identified the brain regions with DC alterations.

METHODS: The experimental data were composed of resting-state functional magnetic resonance imaging (rs-fMRI) from 40 HCs, 40 MDD subjects, and 34 SD subjects. After conducting a one-way analysis of variance, two-sample t-tests were used for further analysis to explore the brain regions with changed DC. Receiver operating characteristic (ROC) curve analysis of single index and composite index features was performed to analyze the distinguishable ability of important brain regions.

RESULTS: For the comparison of MDD vs. HC, increased DC was found in the right superior temporal gyrus (STG) and right inferior parietal lobule (IPL) in the MDD group. For SD vs. HC, the SD group showed a higher DC in the right STG and the right middle temporal gyrus (MTG), and a smaller DC in the left IPL. For MDD vs. SD, increased DC in the right middle frontal gyrus (MFG), right IPL, and left IPL, and decreased DC in the right STG and right MTG was found in the MDD group. With an area under the ROC (AUC) of 0.779, the right STG could differentiate MDD patients from HCs and, with an AUC of 0.704, the right MTG could differentiate MDD patients from SD patients. The three composite indexes had good discriminative ability in each pairwise comparison, with AUCs of 0.803, 0.751, and 0.814 for MDD vs. HC, SD vs. HC, and MDD vs. SD, respectively.

CONCLUSION: Altered DC in the STG, MTG, IPL, and MFG were identified in depression groups. The DC values of these altered regions and their combinations presented good discriminative ability between HC, SD, and MDD. These findings could help to find effective biomarkers and reveal the potential mechanisms of depression.

PMID:36873202 | PMC:PMC9978101 | DOI:10.3389/fpsyt.2023.1084443

J Affect Disord. 2023 Mar 3:S0165-0327(23)00318-X. doi: 10.1016/j.jad.2023.02.146. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebral specialization is an important functional architecture of the human brain. Abnormal cerebral specialization may be the underlying pathogenesis of obsessive-compulsive disorder (OCD). Resting-state functional magnetic resonance imaging (rs-fMRI) was used to show that the specialization pattern of OCD was of great significance for early warning and precise intervention of the disease.

METHOD: The autonomy index (AI) based on the rs-fMRI was calculated to compare brain specializations between 80 OCD patients and 81 matched healthy controls (HCs). In addition, we also correlated the AI alteration patterns with neurotransmitter receptor/transporter densities.

RESULTS: OCD patients showed increased AI in the right insula and right superior temporal gyrus when compared with HCs. In addition, AI differences were associated with serotonin receptors (5-HT1AR and 5HT4R), dopamine D2 receptors, norepinephrine transporters, and metabotropic glutamate receptor densities.

LIMITATIONS: Drug effect; cross-sectional study design; the selection of positron emission tomography template.

CONCLUSIONS: This study showed abnormal specialization patterns in OCD patients, which may lead to the elucidation of the underlying pathological mechanism of the disease.

PMID:36871908 | DOI:10.1016/j.jad.2023.02.146

Neurobiol Dis. 2023 Mar 3:106053. doi: 10.1016/j.nbd.2023.106053. Online ahead of print.

ABSTRACT

PTE is a neurological disorder characterized by recurrent and spontaneous epileptic seizures. PTE is a major public health problem occurring in 2-50% of TBI patients. Identifying PTE biomarkers is crucial for the development of effective treatments. Functional neuroimaging studies in patients with epilepsy and in epileptic rodents have observed that abnormal functional brain activity plays a role in the development of epilepsy. Network representations of complex systems ease quantitative analysis of heterogeneous interactions within a unified mathematical framework. In this work, graph theory was used to study resting state functional magnetic resonance imaging (rs-fMRI) and reveal functional connectivity abnormalities that are associated with seizure development in traumatic brain injury (TBI) patients. We examined rs-fMRI of 75 TBI patients from Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) which aims to identify validated Post-traumatic epilepsy (PTE) biomarkers and antiepileptogenic therapies using multimodal and longitudinal data acquired from 14 international sites. The dataset includes 28 subjects who had at least one late seizure after TBI and 47 subjects who had no seizures within 2 years post-injury. Each subject's neural functional network was investigated by computing the correlation between the low frequency time series of 116 regions of interest (ROIs). Each subject's functional organization was represented as a network consisting of nodes, brain regions, and edges that show the relationship between the nodes. Then, several graph measures concerning the integration and the segregation of the functional brain networks were extracted in order to highlight changes in functional connectivity between the two TBI groups. Results showed that the late seizure-affected group had a compromised balance between integration and segregation and presents functional networks that are hyperconnected, hyperintegrated but at the same time hyposegregated compared with seizure-free patients. Moreover, TBI subjects who developed late seizures had more low betweenness hubs.

PMID:36871641 | DOI:10.1016/j.nbd.2023.106053

Neuroimage Clin. 2023 Feb 27;37:103363. doi: 10.1016/j.nicl.2023.103363. Online ahead of print.

ABSTRACT

Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer's disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N = 886) between 42 and 95 years of age (mean = 70 years). We separated individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the proportion of time each subject spent in each state, called occupancy rate or OCR and frequency of visits. We compared both sFNC and dFNC features across individuals with different genetic risks and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. We also found that AD genetic risk affects whole-brain sFNC and dFNC in women but not men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

PMID:36871405 | DOI:10.1016/j.nicl.2023.103363

Neuroimage Clin. 2023 Mar 1;37:103361. doi: 10.1016/j.nicl.2023.103361. Online ahead of print.

ABSTRACT

OBJECTIVE: We aimed to explore the pathogenesis of traumatic coma related to functional connectivity (FC) within the default mode network (DMN), within the executive control network (ECN) and between the DMN and ECN and to investigate its capacity for predicting awakening.

METHODS: We carried out resting-state functional magnetic resonance imaging (fMRI) examinations on 28 traumatic coma patients and 28 age-matched healthy controls. DMN and ECN nodes were split into regions of interest (ROIs), and node-to-node FC analysis was conducted on individual participants. To identify coma pathogenesis, we compared the pairwise FC differences between coma patients and healthy controls. Meanwhile, we divided the traumatic coma patients into different subgroups based on their clinical outcome scores at 6 months postinjury. Considering the awakening prediction, we calculated the area under the curve (AUC) to evaluate the predictive ability of changed FC pairs.

RESULTS: We found a massive pairwise FC alteration in the patients with traumatic coma compared to the healthy controls [45% (33/74) pairwise FC located in the DMN, 27% (20/74) pairwise FC located in the ECN, and 28% (21/74) pairwise FC located between the DMN and ECN]. Moreover, in the awake and coma groups, there were 67% (12/18) pairwise FC alterations located in the DMN and 33% (6/18) pairwise FC alterations located between the DMN and ECN. We also indicated that pairwise FC that showed a predictive value of 6-month awakening was mainly located in the DMN rather than in the ECN. Specifically, decreased FC between the right superior frontal gyrus and right parahippocampal gyrus (in the DMN) showed the highest predictive ability (AUC = 0.827).

CONCLUSION: In the acute phase of severe traumatic brain injury (sTBI), the DMN plays a more prominent role than the ECN and the DMN-ECN interaction in the emergence of traumatic coma and the prediction of 6-month awakening.

PMID:36871404 | DOI:10.1016/j.nicl.2023.103361

Neurol Sci. 2023 Mar 4. doi: 10.1007/s10072-023-06664-5. Online ahead of print.

ABSTRACT

PURPOSE: To explore the alterations of whole brain functional network using the degree centrality (DC) analysis in neovascular glaucoma (NVG) and the correlation between DC values and NVG clinical indices.

MATERIALS AND METHODS: Twenty NVG patients and twenty normal controls (NC), closely matched in age, sex, and education, were recruited for this study. All subjects underwent comprehensive ophthalmologic examinations and a resting-state functional magnetic resonance imaging (rs-fMRI) scan. The differences in DC values of brain network between NVG and NC groups were analyzed, and correlation analysis was performed to explore the relationships between DC values and clinical ophthalmological indices in NVG group.

RESULTS: Compared with NC group, significantly decreased DC values were found in the left superior occipital gyrus and left postcentral gyrus, while significantly increased DC values in the right anterior cingulate gyrus and left medial frontal gyrus in NVG group. (All P < 0.05, FDR corrected). In the NVG group, the DC value in left superior occipital gyrus showed significantly positive correlations with retinal nerve fiber layer (RNFL) thickness (R = 0.484, P = 0.031) and mean deviation of visual field (MDVF) (R = 0.678, P = 0.001). Meanwhile, the DC value in the left medial frontal gyrus demonstrated significantly negative correlations with RNFL (R = - 0.544, P = 0.013) and MDVF (R = - 0.481, P = 0.032).

CONCLUSIONS: NVG exhibited decreased network degree centrality in visual and sensorimotor brain regions and increased degree centrality in cognitive-emotional processing brain region. Additionally, the DC alterations might be complementary imaging biomarkers to assess disease severity.

PMID:36869275 | DOI:10.1007/s10072-023-06664-5

Neuropsychopharmacology. 2023 Mar 3. doi: 10.1038/s41386-023-01528-0. Online ahead of print.

ABSTRACT

Nicotine dependence is a major predictor of relapse in people with Tobacco Use Disorder (TUD). Accordingly, therapies that reduce nicotine dependence may promote sustained abstinence from smoking. The insular cortex has been identified as a promising target in brain-based therapies for TUD, and has three major sub-regions (ventral anterior, dorsal anterior, and posterior) that serve distinct functional networks. How these subregions and associated networks contribute to nicotine dependence is not well understood, and therefore was the focus of this study. Sixty individuals (28 women; 18-45 years old), who smoked cigarettes daily, rated their level of nicotine dependence (on the Fagerström Test for Nicotine Dependence) and, after abstaining from smoking overnight (~12 h), underwent functional magnetic resonance imaging (fMRI) in a resting state. A subset of these participants (N = 48) also completing a cue-induced craving task during fMRI. Correlations between nicotine dependence and resting-state functional connectivity (RSFC) and cue-induced activation of the major insular sub-regions were evaluated. Nicotine dependence was negatively correlated with connectivity of the left and right dorsal, and left ventral anterior insula with regions within the superior parietal lobule (SPL), including the left precuneus. No relationship between posterior insula connectivity and nicotine dependence was found. Cue-induced activation in the left dorsal anterior insula was positively associated with nicotine dependence and negatively associated with RSFC of the same region with SPL, suggesting that craving-related responsivity in this subregion was greater among participants who were more dependent. These results may inform therapeutic approaches, such as brain stimulation, which may elicit differential clinical outcomes (e.g., dependence, craving) depending on the insular subnetwork that is targeted.

PMID:36869233 | DOI:10.1038/s41386-023-01528-0

Neuropsychologia. 2023 Mar 1:108524. doi: 10.1016/j.neuropsychologia.2023.108524. Online ahead of print.

ABSTRACT

Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about 'self' and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction.

PMID:36868500 | DOI:10.1016/j.neuropsychologia.2023.108524

Clin Neurol Neurosurg. 2023 Feb 28;227:107664. doi: 10.1016/j.clineuro.2023.107664. Online ahead of print.

ABSTRACT

OBJECTIVE: To observe abnormal tinnitus activity by evaluating the amplitude of low-frequency fluctuation (ALFF) changes in the brain was which detected by resting-state functional magnetic resonance imaging (rs-fMRI) in patients with intractable tinnitus before and after repetitive transcranial magnetic stimulation (rTMS). We hypothesized that rTMS could progressively revert local brain function back to a relatively normal range.

METHODS: This prospective observational research study recruited 25 patients with intractable tinnitus, with 28 healthy controls matched by age, sex, and education level. Participants' Tinnitus Handicap Inventory (THI) scores and the visual analog scale (VAS) were used to determine the severity of their tinnitus before and after treatment. We processed the brain spontaneous neural activity of intractable tinnitus patients by ALFF, then, we determined its association with clinically evaluated indicators of intractable tinnitus.

RESULTS: The total and the three sub-modules (functional [F], emotional [E], and catastrophic [C]) score of the THI and VAS in patients with intractable tinnitus decreased after treatment (P < 0.001). The effective rate of tinnitus patients was 66.9%. A few patients had a slight left facial muscle tremor or temporary mild scalp pain during treatment. Compared with healthy controls, participants with tinnitus significantly reduced ALFF within the left and right medial superior frontal gyrus (P < 0.005). After rTMS treatment, the left fusiform gyrus and right superior cerebellar lobe increased ALFF in those with tinnitus (P < 0.005). The changes in THI, VAS, and ALFF were positively correlated (P < 0.05).

CONCLUSION: RTMS is effective in the treatment of tinnitus. It significantly reduces the THI/VAS score and improves the symptoms of tinnitus. No serious adverse reaction during rTMS were reported. The changes in the left fusiform gyrus and right superior part of the cerebellum may explain the mechanism of rTMS treatment in intractable tinnitus.

PMID:36868087 | DOI:10.1016/j.clineuro.2023.107664

Front Hum Neurosci. 2023 Feb 14;17:1076873. doi: 10.3389/fnhum.2023.1076873. eCollection 2023.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is a childhood mental health disorder that often persists to adulthood and is characterized by inattentive, hyperactive, or impulsive behaviors. This study investigated structural and effective connectivity differences through voxel-based morphometry (VBM) and Granger causality analysis (GCA) across child, adolescent, and adult ADHD patients. Structural and functional MRI data consisting of 35 children (8.64 ± 0.81 years), 40 adolescents (14.11 ± 1.83 years), and 39 adults (31.59 ± 10.13 years) was obtained from New York University Child Study Center for the ADHD-200 and UCLA dataset. Structural differences in the bilateral pallidum, bilateral thalamus, bilateral insula, superior temporal cortex, and the right cerebellum were observed among the three ADHD groups. The right pallidum was positively correlated with disease severity. The right pallidum as a seed precedes and granger causes the right middle occipital cortex, bilateral fusiform, left postcentral gyrus, left paracentral lobule, left amygdala, and right cerebellum. Also, the anterior cingulate cortex, prefrontal cortex, left cerebellum, left putamen, left caudate, bilateral superior temporal pole, middle cingulate cortex, right precentral gyrus, and the left supplementary motor area demonstrated causal effects on the seed region. In general, this study showed the structural differences and the effective connectivity of the right pallidum amongst the three ADHD age groups. Our work also highlights the evidence of the frontal-striatal-cerebellar circuits in ADHD and provides new insights into the effective connectivity of the right pallidum and the pathophysiology of ADHD. Our results further demonstrated that GCA could effectively explore the interregional causal relationship between abnormal regions in ADHD.

PMID:36866118 | PMC:PMC9971633 | DOI:10.3389/fnhum.2023.1076873

bioRxiv. 2023 Feb 22:2023.02.21.529425. doi: 10.1101/2023.02.21.529425. Preprint.

ABSTRACT

Working memory (WM) is a crucial resource for temporary memory storage and the guiding of ongoing behavior. N-methyl-D-aspartate glutamate receptors (NMDARs) are thought to support the neural underpinnings of WM. Ketamine is an NMDAR antagonist that has cognitive and behavioral effects at subanesthetic doses. To shed light on subanesthetic ketamine effects on brain function, we employed a multimodal imaging design, combining gas-free calibrated functional magnetic resonance imaging (fMRI) measurement of oxidative metabolism (CMRO 2 ), resting-state cortical functional connectivity assessed with fMRI, and WM-related fMRI. Healthy subjects participated in two scan sessions in a randomized, double-blind, placebo-controlled design. Ketamine increased CMRO 2 and cerebral blood flow (CBF) in prefrontal cortex (PFC) and other cortical regions. However, resting-state cortical functional connectivity was not affected. Ketamine did not alter CBF-CMRO 2 coupling brain-wide. Higher levels of basal CMRO 2 were associated with lower task-related PFC activation and WM accuracy impairment under both saline and ketamine conditions. These observations suggest that CMRO 2 and resting-state functional connectivity index distinct dimensions of neural activity. Ketamine’s impairment of WM-related neural activity and performance appears to be related to its ability to produce cortical metabolic activation. This work illustrates the utility of direct measurement of CMRO 2 via calibrated fMRI in studies of drugs that potentially affect neurovascular and neurometabolic coupling.

PMID:36865249 | PMC:PMC9980048 | DOI:10.1101/2023.02.21.529425

bioRxiv. 2023 Feb 27:2023.02.22.529400. doi: 10.1101/2023.02.22.529400. Preprint.

ABSTRACT

How the emergent functional connectivity (FC) relates to the underlying anatomy (structural connectivity, SC) is one of the biggest questions of modern neuroscience. At the macro-scale level, no one-to-one correspondence between structural and functional links seems to exist. And we posit that to better understand their coupling, two key aspects should be taken into account: the directionality of the structural connectome and the limitations of describing network functions in terms of FC. Here, we employed an accurate directed SC of the mouse brain obtained by means of viral tracers, and related it with single-subject effective connectivity (EC) matrices computed by applying a recently developed DCM to whole-brain resting-state fMRI data. We analyzed how SC deviates from EC and quantified their couplings by conditioning both on the strongest SC links and EC links. We found that when conditioning on the strongest EC links, the obtained coupling follows the unimodal-transmodal functional hierarchy. Whereas the reverse is not true, as there are strong SC links within high-order cortical areas with no corresponding strong EC links. This mismatch is even more clear across networks. Only the connections within sensory motor networks align both in terms of effective and structural strength.

PMID:36865122 | PMC:PMC9980133 | DOI:10.1101/2023.02.22.529400

J Affect Disord. 2023 Feb 28:S0165-0327(23)00286-0. doi: 10.1016/j.jad.2023.02.118. Online ahead of print.

ABSTRACT

BACKGROUND: The advances in resting-state functional magnetic resonance imaging techniques motivate parsing heterogeneity in major depressive disorder (MDD) through neurophysiological subtypes (i.e., biotypes). Based on graph theories, researchers have observed the functional organization of the human brain as a complex system with modular structures and have found wide-spread but variable MDD-related abnormality regarding the modules. The evidence implies the possibility of identifying biotypes using high-dimensional functional connectivity (FC) data in ways that suit the potentially multifaceted biotypes taxonomy.

METHODS: We proposed a multiview biotype discovery framework that involves theory-driven feature subspace partition (i.e., "view") and independent subspace clustering. Six views were defined using intra- and intermodule FC regarding three MDD focal modules (i.e., the sensory-motor system, default mode network, and subcortical network). For robust biotypes, the framework was applied to a large multisite sample (805 MDD participants and 738 healthy controls).

RESULTS: Two biotypes were stably obtained in each view, respectively characterized by significantly increased and decreased FC compared to healthy controls. These view-specific biotypes promoted the diagnosis of MDD and showed different symptom profiles. By integrating the view-specific biotypes into biotype profiles, a broad spectrum in the neural heterogeneity of MDD and its separation from symptom-based subtypes was further revealed.

LIMITATIONS: The power of clinical effects is limited and the cross-sectional nature cannot predict the treatment effects of the biotypes.

CONCLUSIONS: Our findings not only contribute to the understanding of heterogeneity in MDD, but also provide a novel subtyping framework that could transcend current diagnostic boundaries and data modality.

PMID:36863463 | DOI:10.1016/j.jad.2023.02.118

Front Neurosci. 2023 Feb 13;17:1153035. doi: 10.3389/fnins.2023.1153035. eCollection 2023.

NO ABSTRACT

PMID:36860619 | PMC:PMC9969151 | DOI:10.3389/fnins.2023.1153035

Dev Cogn Neurosci. 2023 Feb 17;60:101216. doi: 10.1016/j.dcn.2023.101216. Online ahead of print.

ABSTRACT

The default mode network (DMN) plays a crucial role in internal self-processing, rumination, and social functions. Disruptions to DMN connectivity have been linked with early adversity and the emergence of psychopathology in adolescence and early adulthood. Herein, we investigate how subclinical psychiatric symptoms can impact DMN functional connectivity during the pubertal transition. Resting-state fMRI data were collected annually from 190 typically-developing youth (9-15 years-old) at three timepoints and within-network DMN connectivity was computed. We used latent growth curve modeling to determine how self-reported depressive and posttraumatic stress symptoms predicted rates of change in DMN connectivity over the three-year period. In the baseline model without predictors, we found no systematic changes in DMN connectivity over time. However, significant modulation emerged after adding psychopathology predictors; greater depressive symptomatology was associated with significant decreases in connectivity over time, whereas posttraumatic stress symptoms were associated with significant increases in connectivity over time. Follow-up analyses revealed that these effects were driven by connectivity changes involving the dorsal medial prefrontal cortex subnetwork. In conclusion, these data suggest that subclinical depressive and posttraumatic symptoms alter the trajectory of DMN connectivity, which may indicate that this network is a nexus of clinical significance in mental health disorders.

PMID:36857850 | DOI:10.1016/j.dcn.2023.101216

Epilepsia. 2023 Feb 28. doi: 10.1111/epi.17565. Online ahead of print.

ABSTRACT

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study is to leverage network neuroscience to better understand the interictal whole brain network of bilateral temporal lobe epilepsy (BiTLE).

METHODS: In this study, using a multicenter resting state functional MRI (rs-fMRI) dataset, we constructed whole brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting-state, whole-brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density versus each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration-segregation axis", by combining whole brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)-based dimensionality reduction.

RESULTS: Compared to UTLE, BiTLE had decreased global efficiency (p=0.031), and decreased whole brain average participation coefficient across a range of network densities (p=0.019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p=0.020). Differences in network properties separate BiTLE and UTLE along the integration-segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration-segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE.

SIGNIFICANCE: Increased interictal whole brain network segregation, as measured by rs-fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non-invasively identifying this patient population prior to intracranial EEG or device implantation.

PMID:36855286 | DOI:10.1111/epi.17565

Hum Brain Mapp. 2023 Feb 28. doi: 10.1002/hbm.26251. Online ahead of print.

ABSTRACT

Schizophrenia (SZ) is a severe mental disorder characterized by failure of functional integration (aka dysconnection) across the brain. Recent functional connectivity (FC) studies have adopted functional parcellations to define subnetworks of large-scale networks, and to characterize the (dys)connection between them, in normal and clinical populations. While FC examines statistical dependencies between observations, model-based effective connectivity (EC) can disclose the causal influences that underwrite the observed dependencies. In this study, we investigated resting state EC within seven large-scale networks, in 66 SZ and 74 healthy subjects from a public dataset. The results showed that a remarkable 33% of the effective connections (among subnetworks) of the cognitive control network had been pathologically modulated in SZ. Further dysconnection was identified within the visual, default mode and sensorimotor networks of SZ subjects, with 24%, 20%, and 11% aberrant couplings. Overall, the proportion of discriminative connections was remarkably larger in EC (24%) than FC (1%) analysis. Subsequently, to study the neural correlates of impaired cognition in SZ, we conducted a canonical correlation analysis between the EC parameters and the cognitive scores of the patients. As such, the self-inhibitions of supplementary motor area and paracentral lobule (in the sensorimotor network) and the excitatory connection from parahippocampal gyrus to inferior temporal gyrus (in the cognitive control network) were significantly correlated with the social cognition, reasoning/problem solving and working memory capabilities of the patients. Future research can investigate the potential of whole-brain EC as a biomarker for diagnosis of brain disorders and for neuroimaging-based cognitive assessment.

PMID:36852654 | DOI:10.1002/hbm.26251

Hum Brain Mapp. 2023 Feb 28. doi: 10.1002/hbm.26245. Online ahead of print.

ABSTRACT

Brain arteriovenous malformations (AVMs) may involve language areas but usually do not lead to aphasia. This study evaluated resting-state functional alterations and investigated the language reorganization mechanism in AVM patients. Thirty-nine patients with AVMs involving language areas and 32 age- and sex-matched healthy controls were prospectively enrolled. The AVM patients were categorized into three subgroups according to lesion location: the frontal (15 patients), temporal (14 patients), and parietal subgroups (10 patients). All subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI), and the amplitude of low-frequency fluctuation (ALFF) approach was applied to analyze rs-fMRI data. Language abilities were normal in all participants based on the Western Aphasia Battery. Compared with those of healthy subjects, ALFF values significantly increased (FDR corrected p < .01) in the anterior part of the right putamen in the frontal AVM subgroup, in the posterior part of the right inferior and middle temporal gyrus in the temporal AVM subgroup, and in the inferior lateral part of the left cerebellar hemisphere (lobule VIII) and the right inferior parietal lobule in the parietal AVM subgroup. Functional annotation using Neurosynth indicated that the ALFF t-map was only significantly positively associated with the language-related domain (FDR corrected p < .01). In patients with AVMs involving the language cortex, language network reorganization occurs to maintain normal language abilities. The brain areas recruited into the reorganized language network were located in the right cerebral and left cerebellar hemispheres, both of which are nondominant hemispheres. Differences in lesion location led to distinct reorganization patterns.

PMID:36852640 | DOI:10.1002/hbm.26245

Hum Brain Mapp. 2023 Feb 28. doi: 10.1002/hbm.26248. Online ahead of print.

ABSTRACT

While verbal memory is among the most compromised cognitive domains in schizophrenia (SZ), its neural substrates remain elusive. Here, we explored the structural and functional brain network correlates of verbal memory impairment in SZ. We acquired diffusion and resting-state functional MRI data of 49 SZ patients, classified as having preserved (VMP, n = 22) or impaired (VMI, n = 26) verbal memory based on the List Learning task, and 55 healthy controls (HC). Structural and functional connectivity matrices were obtained and analyzed to assess associations with disease status (SZ vs. HC) and verbal memory impairment (VMI vs. VMP) using two complementary data-driven approaches: threshold-free network-based statistics (TFNBS) and hybrid connectivity independent component analysis (connICA). TFNBS showed altered connectivity in SZ patients compared with HC (p < .05, FWER-corrected), with distributed structural changes and functional reorganization centered around sensorimotor areas. Specifically, functional connectivity was reduced within the visual and somatomotor networks and increased between visual areas and associative and subcortical regions. Only a tiny cluster of increased functional connectivity between visual and bilateral parietal attention-related areas correlated with verbal memory dysfunction. Hybrid connICA identified four robust traits, representing fundamental patterns of joint structural-functional connectivity. One of these, mainly capturing the functional connectivity profile of the visual network, was significantly associated with SZ (HC vs. SZ: Cohen's d = .828, p < .0001) and verbal memory impairment (VMP vs. VMI: Cohen's d = -.805, p = .01). We suggest that aberrant connectivity of sensorimotor networks may be a key connectomic signature of SZ and a putative biomarker of SZ-related verbal memory impairment, in consistency with bottom-up models of cognitive disruption.

PMID:36852587 | DOI:10.1002/hbm.26248