Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

A prospective study on EEG default mode network associated with subsequent posttraumatic stress disorder following sexual assault

This study aimed to explore the predictors of posttraumatic stress disorder (PTSD) in women who have recently experienced sexual assault, by examining psychological and neurophysiological factors using a prospective design with resting-state electroencephalogram (EEG) functional connectivity. The study enrolled 33 women who had been recently traumatized by sexual assault and conducted assessments within a month of the trauma. These survivors were evaluated for PTSD three months later and were classified into two groups: PTSD positive (n = 12) and PTSD negative (n = 21). They were compared to two control groups comprising women who had not experienced any extremely traumatic events: 25 with depression and 25 healthy controls. The evaluation focused on resting-state EEG functional connectivity within default mode network (DMN) using small-worldness (SW), based on graph theory. We also assessed self-reported levels of depression, anxiety, anger, and executive functions. The findings indicated that survivors who developed PTSD three months post-trauma exhibited higher anxiety levels and reduced DMN SW in the beta 3 frequency, compared to those who did not develop PTSD. Contrary to expectations, survivors without PTSD showed decreased executive functioning and lower prefrontal centrality compared to those with PTSD. This study underscores the importance of early assessment and intervention for sexual assault survivors at risk of developing PTSD.

Correlation between Thalamocortical Tract and Default Mode Network with Consciousness Levels in Hypoxic-Ischemic Brain Injury Patients: A Comparative Study Using the Coma Recovery Scale-Revised

BACKGROUND The thalamocortical tract (TCT) links nerve fibers between the thalamus and cerebral cortex, relaying motor/sensory information. The default mode network (DMN) comprises bilateral, symmetrical, isolated cortical regions of the lateral and medial parietal and temporal brain cortex. The Coma Recovery Scale-Revised (CRS-R) is a standardized neurobehavioral assessment of disorders of consciousness (DOC). In the present study, 31 patients with hypoxic-ischemic brain injury (HI-BI) were compared for changes in the TCT and DMN with consciousness levels assessed using the CRS-R. MATERIAL AND METHODS In this retrospective study, 31 consecutive patients with HI-BI (17 DOC,14 non-DOC) and 17 age- and sex-matched normal control subjects were recruited. Magnetic resonance imaging was used to diagnose HI-BI, and the CRS-R was used to evaluate consciousness levels at the time of diffusion tensor imaging (DTI). The fractional anisotropy (FA) values and tract volumes (TV) of the TCT and DMN were compared. RESULTS In patients with DOC, the FA values and TV of both the TCT and DMN were significantly lower compared to those of patients without DOC and the control subjects (p<0.05). When comparing the non-DOC and control groups, the TV of the TCT and DMN were significantly lower in the non-DOC group (p<0.05). Moreover, the CRS-R score had strong positive correlations with the TV of the TCT (r=0.501, p<0.05), FA of the DMN (r=0.532, p<0.05), and TV of the DMN (r=0.501, p<0.05) in the DOC group. CONCLUSIONS This study suggests that both the TCT and DMN exhibit strong correlations with consciousness levels in DOC patients with HI-BI.

Functional alterations of the brain default mode network and somatosensory system in trigeminal neuralgia

Mapping the localization of the functional brain regions in trigeminal neuralgia (TN) patients is still lacking. The study aimed to explore the functional brain alterations and influencing factors in TN patients using functional brain imaging techniques. All participants underwent functional brain imaging to collect resting-state brain activity. The significant differences in regional homogeneity (ReHo) and amplitude of low frequency (ALFF) between the TN and control groups were calculated. After familywise error (FWE) correction, the differential brain regions in ReHo values between the two groups were mainly located in bilateral middle frontal gyrus, bilateral inferior cerebellum, right superior orbital frontal gyrus, right postcentral gyrus, left inferior temporal gyrus, left middle temporal gyrus, and left gyrus rectus. The differential brain regions in ALFF values between the two groups were mainly located in the left triangular inferior frontal gyrus, left supplementary motor area, right supramarginal gyrus, and right middle frontal gyrus. With the functional impairment of the central pain area, the active areas controlling memory and emotion also change during the progression of TN. There may be different central mechanisms in TN patients of different sexes, affected sides, and degrees of nerve damage. The exact central mechanisms remain to be elucidated.

MicroRNA-124 influenced depressive symptoms via large-scale brain connectivity in major depressive disorder patients

This study aimed to investigate the neurobiological mechanisms by which microRNA 124 (miR-124) is involved in major depressive disorder (MDD). We enrolled 53 untreated MDD patients and 38 healthy control (HC) subjects who completed behavior assessments and resting-state functional MRI (rs-fMRI) scans. MiR-124 expression levels were detected in the peripheral blood of all participants. We determined that miR-124 levels could influence depressive symptoms via disrupted large-scale intrinsic intra- and internetwork connectivity, including the default mode network (DMN)-DMN, dorsal attention network (DAN)-salience network (SN), and DAN-cingulo-opercular network (CON). This study deepens our understanding of how miR-124 dysregulation contributes to depression.

Associations of conservatism and jumping to conclusions biases with aberrant salience and default mode network

AIM

While conservatism bias refers to the human need for more evidence for decision-making than rational thinking expects, the jumping to conclusions (JTC) bias refers to the need for less evidence among individuals with schizophrenia/delusion compared to healthy people. Although the hippocampus-midbrain-striatal aberrant salience system and the salience, default mode (DMN), and frontoparietal networks ("triple networks") are implicated in delusion/schizophrenia pathophysiology, the associations between conservatism/JTC and these systems/networks are unclear.

METHODS

Thirty-seven patients with schizophrenia and 33 healthy controls performed the beads task, with large and small numbers of bead draws to decision (DTD) indicating conservatism and JTC, respectively. We performed independent component analysis (ICA) of resting functional magnetic resonance imaging (fMRI) data. For systems/networks above, we investigated interactions between diagnosis and DTD, and main effects of DTD. We similarly applied ICA to structural and diffusion MRI to explore the associations between DTD and gray/white matter.

RESULTS

We identified a significant main effect of DTD with functional connectivity between the striatum and DMN, which was negatively correlated with delusion severity in patients, indicating that the greater the anti-correlation between these networks, the stronger the JTC and delusion. We further observed the main effects of DTD on a gray matter network resembling the DMN, and a white matter network connecting the functional and gray matter networks (all P < 0.05, family-wise error [FWE] correction). Function and gray/white matter showed no significant interactions.

CONCLUSION

Our results support the novel association of conservatism and JTC biases with aberrant salience and default brain mode.

Mindfulness-based cognitive therapy neurobiology in treatment-resistant obsessive-compulsive disorder: A domain-related resting-state networks approach

Mindfulness-based cognitive therapy (MBCT) stands out as a promising augmentation psychological therapy for patients with obsessive-compulsive disorder (OCD). To identify potential predictive and response biomarkers, this study examines the relationship between clinical domains and resting-state network connectivity in OCD patients undergoing a 3-month MBCT programme. Twelve OCD patients underwent two resting-state functional magnetic resonance imaging sessions at baseline and after the MBCT programme. We assessed four clinical domains: positive affect, negative affect, anxiety sensitivity, and rumination. Independent component analysis characterised resting-state networks (RSNs), and multiple regression analyses evaluated brain-clinical associations. At baseline, distinct network connectivity patterns were found for each clinical domain: parietal-subcortical, lateral prefrontal, medial prefrontal, and frontal-occipital. Predictive and response biomarkers revealed significant brain-clinical associations within two main RSNs: the ventral default mode network (vDMN) and the frontostriatal network (FSN). Key brain nodes -the precuneus and the frontopolar cortex- were identified within these networks. MBCT may modulate vDMN and FSN connectivity in OCD patients, possibly reducing symptoms across clinical domains. Each clinical domain had a unique baseline brain connectivity pattern, suggesting potential symptom-based biomarkers. Using these RSNs as predictors could enable personalised treatments and the identification of patients who would benefit most from MBCT.

Neurocognition and brain functional connectivity in a non-clinical population-based sample with psychotic experiences

We characterized the neurocognitive profile of communed-based individuals and unaffected siblings of patients with psychosis from Brazil reporting psychotic experiences (PEs). We also analyzed associations between PEs and the intra and inter-functional connectivity (FC) in the Default Mode Network (DMN), the Fronto-Parietal Network (FPN) and the Salience Network (SN) measured by functional magnetic resonance imaging. The combined sample of communed-based individuals and unaffected siblings of patients with psychosis comprised 417 (neurocognition) and 85 (FC) volunteers who were divided as having low (<75th percentile) and high (≥75th percentile) PEs (positive, negative, and depressive dimensions) assessed by the Community Assessment of Psychic Experiences. The neurocognitive profile and the estimated current brief intellectual quotient (IQ) were assessed using the digit symbol (processing speed), arithmetic (working memory), block design (visual learning) and information (verbal learning) subtests of Wechsler Adult Intelligence Scale-third edition. Logistic regression models were performed for neurocognitive analysis. For neuroimaging, we used the CONN toolbox to assess FC between the specified regions, and ROI-to-ROI analysis. In the combined sample, high PEs (all dimensions) were related to lower processing speed performance. High negative PEs were related to poor visual learning performance and lower IQ, while high depressive PEs were associated with poor working memory performance. Those with high negative PEs presented FPN hypoconnectivity between the right and left lateral prefrontal cortex. There were no associations between PEs and the DMN and SN FC. Brazilian individuals with high PEs showed neurocognitive impairments like those living in wealthier countries. Hypoconnectivity in the FPN in a community sample with high PEs is coherent with the hypothesis of functional dysconnectivity in schizophrenia.

Data-driven connectivity profiles relate to smoking cessation outcomes

At a group level, nicotine dependence is linked to differences in resting-state functional connectivity (rs-FC) within and between three large-scale brain networks: the salience network (SN), default mode network (DMN), and frontoparietal network (FPN). Yet, individuals may display distinct patterns of rs-FC that impact treatment outcomes. This study used a data-driven approach, Group Iterative Multiple Model Estimation (GIMME), to characterize shared and person-specific rs-FC features linked with clinically-relevant treatment outcomes. 49 nicotine-dependent adults completed a resting-state fMRI scan prior to a two-week smoking cessation attempt. We used GIMME to identify group, subgroup, and individual-level networks of SN, DMN, and FPN connectivity. Regression models assessed whether within- and between-network connectivity of individual rs-FC models was associated with baseline cue-induced craving, and craving and use of regular cigarettes (i.e., "slips") during cessation. As a group, participants displayed shared patterns of connectivity within all three networks, and connectivity between the SN-FPN and DMN-SN. However, there was substantial heterogeneity across individuals. Individuals with greater within-network SN connectivity experienced more slips during treatment, while individuals with greater DMN-FPN connectivity experienced fewer slips. Individuals with more anticorrelated DMN-SN connectivity reported lower craving during treatment, while SN-FPN connectivity was linked to higher craving. In conclusion, in nicotine-dependent adults, GIMME identified substantial heterogeneity within and between the large-scale brain networks. Individuals with greater SN connectivity may be at increased risk for relapse during treatment, while a greater positive DMN-FPN and negative DMN-SN connectivity may be protective for individuals during smoking cessation treatment.

Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder

AIM

The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD.

METHODS

We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways.

RESULTS

Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD.

CONCLUSION

The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.

The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study

The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.

The default mode network is associated with changes in internalizing and externalizing problems differently in adolescent boys and girls

Internalizing and externalizing problems that emerge during adolescence differentially increase boys' and girls' risk for developing psychiatric disorders. It is not clear, however, whether there are sex differences in the intrinsic functional architecture of the brain that underlie changes in the severity of internalizing and externalizing problems in adolescents. Using resting-state fMRI data and self-reports of behavioral problems obtained from 128 adolescents (73 females; 9-14 years old) at two timepoints, we conducted multivoxel pattern analysis to identify resting-state functional connectivity markers at baseline that predict changes in the severity of internalizing and externalizing problems in boys and girls 2 years later. We found sex-differentiated involvement of the default mode network in changes in internalizing and externalizing problems. Whereas changes in internalizing problems were associated with the dorsal medial subsystem in boys and with the medial temporal subsystem in girls, changes in externalizing problems were predicted by hyperconnectivity between core nodes of the DMN and frontoparietal network in boys and hypoconnectivity between the DMN and affective networks in girls. Our results suggest that different neural mechanisms predict changes in internalizing and externalizing problems in adolescent boys and girls and offer insights concerning mechanisms that underlie sex differences in the expression of psychopathology in adolescence.

Impaired topology and connectivity of grey matter structural networks in major depressive disorder: evidence from a multi-site neuroimaging data-set

BACKGROUND

Major depressive disorder (MDD) has been increasingly understood as a disruption of brain connectome. Investigating grey matter structural networks with a large sample size can provide valuable insights into the structural basis of network-level neuropathological underpinnings of MDD.

AIMS

Using a multisite MRI data-set including nearly 2000 individuals, this study aimed to identify robust topology and connectivity abnormalities of grey matter structural network linked to MDD and relevant clinical phenotypes.

METHOD

A total of 955 MDD patients and 1009 healthy controls were included from 23 sites. Individualised structural covariance networks (SCN) were established based on grey matter volume maps. Following data harmonisation, network topological metrics and focal connectivity were examined for group-level comparisons, individual-level classification performance and association with clinical ratings. Various validation strategies were applied to confirm the reliability of findings.

RESULTS

Compared with healthy controls, MDD individuals exhibited increased global efficiency, abnormal regional centralities (i.e. thalamus, precentral gyrus, middle cingulate cortex and default mode network) and altered circuit connectivity (i.e. ventral attention network and frontoparietal network). First-episode drug-naive and recurrent patients exhibited different patterns of deficits in network topology and connectivity. In addition, the individual-level classification of topological metrics outperforms that of structural connectivity. The thalamus-insula connectivity was positively associated with the severity of depressive symptoms.

CONCLUSIONS

Based on this high-powered data-set, we identified reliable patterns of impaired topology and connectivity of individualised SCN in MDD and relevant subtypes, which adds to the current understanding of neuropathology of MDD and might guide future development of diagnostic and therapeutic markers.

Functional gradients reveal cortical hierarchy changes in multiple sclerosis

Functional gradient (FG) analysis represents an increasingly popular methodological perspective for investigating brain hierarchical organization but whether and how network hierarchy changes concomitant with functional connectivity alterations in multiple sclerosis (MS) has remained elusive. Here, we analyzed FG components to uncover possible alterations in cortical hierarchy using resting-state functional MRI (rs-fMRI) data acquired in 122 MS patients and 97 healthy control (HC) subjects. Cortical hierarchy was assessed by deriving regional FG scores from rs-fMRI connectivity matrices using a functional parcellation of the cerebral cortex. The FG analysis identified a primary (visual-to-sensorimotor) and a secondary (sensory-to-transmodal) component. Results showed a significant alteration in cortical hierarchy as indexed by regional changes in FG scores in MS patients within the sensorimotor network and a compression (i.e., a reduced standard deviation across all cortical parcels) of the sensory-transmodal gradient axis, suggesting disrupted segregation between sensory and cognitive processing. Moreover, FG scores within limbic and default mode networks were significantly correlated (ρ = 0.30 $$ \rho =0.30 $$ , p < .005 after Bonferroni correction for both) with the symbol digit modality test (SDMT) score, a measure of information processing speed commonly used in MS neuropsychological assessments. Finally, leveraging supervised machine learning, we tested the predictive value of network-level FG features, highlighting the prominent role of the FG scores within the default mode network in the accurate prediction of SDMT scores in MS patients (average mean absolute error of 1.22 ± 0.07 points on a hold-out set of 24 patients). Our work provides a comprehensive evaluation of FG alterations in MS, shedding light on the hierarchical organization of the MS brain and suggesting that FG connectivity analysis can be regarded as a valuable approach in rs-fMRI studies across different MS populations.

Sleep spindle architecture associated with distinct clinical phenotypes in older adults at risk for dementia

Sleep spindles are a hallmark of non-REM sleep and play a fundamental role in memory consolidation. Alterations in these spindles are emerging as sensitive biomarkers for neurodegenerative diseases of ageing. Understanding the clinical presentations associated with spindle alterations may help to elucidate the functional role of these distinct electroencephalographic oscillations and the pathophysiology of sleep and neurodegenerative disorders. Here, we use a data-driven approach to examine the sleep, memory and default mode network connectivity phenotypes associated with sleep spindle architecture in older adults (mean age = 66 years). Participants were recruited from a specialist clinic for early diagnosis and intervention for cognitive decline, with a proportion showing mild cognitive deficits on neuropsychological testing. In a sample of 88 people who underwent memory assessment, overnight polysomnography and resting-state fMRI, a k-means cluster analysis was applied to spindle measures of interest: fast spindle density, spindle duration and spindle amplitude. This resulted in three clusters, characterised by preserved spindle architecture with higher fast spindle density and longer spindle duration (Cluster 1), and alterations in spindle architecture (Clusters 2 and 3). These clusters were further characterised by reduced memory (Clusters 2 and 3) and nocturnal hypoxemia, associated with sleep apnea (Cluster 3). Resting-state fMRI analysis confirmed that default mode connectivity was related to spindle architecture, although directionality of this relationship differed across the cluster groups. Together, these results confirm a diversity in spindle architecture in older adults, associated with clinically meaningful phenotypes, including memory function and sleep apnea. They suggest that resting-state default mode connectivity during the awake state can be associated with sleep spindle architecture; however, this is highly dependent on clinical phenotype. Establishing relationships between clinical and neuroimaging features and sleep spindle alterations will advance our understanding of the bidirectional relationships between sleep changes and neurodegenerative diseases of ageing.

Selective Impairment of Long-Range Default Mode Network Functional Connectivity as a Biomarker for Preclinical Alzheimer's Disease in People with Down Syndrome

BACKGROUND

Down syndrome (DS) is associated with increased risk for Alzheimer's disease (AD). In neurotypical individuals, clinical AD is preceded by reduced resting state functional connectivity in the default mode network (DMN), but it is unknown whether changes in DMN connectivity predict clinical onset of AD in DS.

OBJECTIVE

Does lower DMN functional connectivity predict clinical onset of AD and cognitive decline in people with DS?

METHODS

Resting state functional MRI (rsfMRI), longitudinal neuropsychological, and clinical assessment data were collected on 15 nondemented people with DS (mean age = 51.66 years, SD = 5.34 years, range = 42-59 years) over four years, during which 4 transitioned to dementia. Amyloid-β (Aβ) PET data were acquired on 13 of the 15 participants. Resting state fMRI, neuropsychological, and clinical assessment data were also acquired on an independent, slightly younger unimpaired sample of 14 nondemented people with DS (mean age = 44.63 years, SD = 7.99 years, range = 38-61 years).

RESULTS

Lower functional connectivity between long-range but not short-range DMN regions predicts AD diagnosis and cognitive decline in people with DS. Aβ accumulation in the inferior parietal cortex is associated with lower regional DMN functional connectivity.

CONCLUSION

Reduction of long-range DMN connectivity is a potential biomarker for AD in people with DS that precedes and predicts clinical conversion.

Facing successfully high mental workload and stressors: An fMRI study

The present fMRI study aimed at highlighting patterns of brain activations and autonomic activity when confronted with high mental workload and the threat of auditory stressors. Twenty participants performed a complex cognitive task in either safe or aversive conditions. Our results showed that increased mental workload induced recruitment of the lateral frontoparietal executive control network (ECN), along with disengagement of medial prefrontal and posterior cingulate regions of the default mode network (DMN). Mental workload also elicited an increase in heart rate and pupil diameter. Task performance did not decrease under the threat of stressors, most likely due to efficient inhibition of auditory regions, as reflected by a large decrement of activity in the superior temporal gyri. The threat of stressors was also accompanied with deactivations of limbic regions of the salience network (SN), possibly reflecting emotional regulation mechanisms through control from dorsal medial prefrontal and parietal regions, as indicated by functional connectivity analyses. Meanwhile, the threat of stressors induced enhanced ECN activity, likely for improved attentional and cognitive processes toward the task, as suggested by increased lateral prefrontal and parietal activations. These fMRI results suggest that measuring the balance between ECN, SN, and DMN recruitment could be used for objective mental state assessment. In this sense, an extra recruitment of task‐related regions and a high ratio of lateral versus medial prefrontal activity may represent a relevant marker of increased but efficient mental effort, while the opposite may indicate a disengagement from the task due to mental overload and/or stressors.

Dynamic brain connectome and high risk of mental problem in clinical nurses

With the growing population and rapid change in the social environment, nurses in China are suffering from high rates of stress; however, the neural mechanism underlying this occupation related stress is largely unknown. In this study, mental status was determined for 81 nurses and 61 controls using the Symptom Checklist 90 (SCL-90) scale. A subgroup (n = 57) was further scanned by resting-state functional MRI with two sessions. Based on the SCL-90 scale, "somatic complaints" and "diet/sleeping" exhibited the most prominent difference between nurses and controls. This mental health change in nurses was further supported by the spatial independent component analysis on functional MRI data. First, dynamic functional connectome analysis identified two discrete connectivity configurations (States I and II). Controls had more time in the State I than II, while the nurses had more time in the State II than I. Second, nurses showed a similar static network topology as controls, but altered dynamic properties. Third, the symptom-imaging correlation analysis suggested the functional alterations in nurses as potential imaging biomarkers indicating a high risk for "diet/sleeping" problems. In summary, this study emphasized the high risk of mental deficits in nurses and explored the underlying neural mechanism using dynamic brain connectome, which provided valuable information for future psychological intervention.

Mindfulness and Attention Deficit Hyperactivity Disorder: A Neuropsychological Perspective

ABSTRACT

Understanding the underlying mechanisms of mindfulness has been a hot topic in recent years, not only in clinical fields but also in neuroscience. Most neuroimaging findings demonstrate that critical brain regions involved in mindfulness are responsible for cognitive functions and mental states. However, the brain is a complex system operating via multiple circuits and networks, rather than isolated brain regions solely responsible for specific functions. Mindfulness-based treatments for attention deficit hyperactivity disorder (ADHD) have emerged as promising adjunctive or alternative intervention approaches. We focus on four key brain circuits associated with mindfulness practices and effects on symptoms of ADHD and its cognitive dysfunction, including executive attention circuit, sustained attention circuit, impulsivity circuit, and hyperactivity circuit. We also expand our discussion to identify three key brain networks associated with mindfulness practices, including central executive network, default mode network, and salience network. We conclude by suggesting that more research efforts need to be devoted into identifying putative neuropsychological mechanisms of mindfulness on how it alleviates ADHD symptoms.

Coupling relationship between glucose and oxygen metabolisms to differentiate preclinical Alzheimer's disease and normal individuals

The discovery of preclinical Alzheimer's disease (preAD) provides a wide time window for the early intervention of AD. The coupling relationships between glucose and oxygen metabolisms from hybrid PET/MRI can provide complementary information on the brain's physiological state for preAD. In this study, we purpose to explore the change of coupling relationship among 27 normal controls (NCs), 20 preADs, and 15 cognitive impairments (CIs). For each subject, we calculated the Spearman partial correlation between the fractional amplitude of low-frequency fluctuations (fALFF) and the regional homogeneity (ReHo) from functional image (fMRI), and the standard uptake value ratio (SUVR) from [18F] fluorodeoxyglucose positron emission tomography (18 F-FDG PET), in the whole-brain and default mode network (DMN) as a novel potential biomarker. The diagnostic performance of this biomarker was evaluated by the receiver operating characteristic analysis. Significant Spearman correlations between the FDG SUVR and the fALFF/ReHo were found in 98% of subjects. For the DMN-based biomarker, there was a significant decreasing trend for the preAD and CI groups compared to the NC group, whereas no significant difference in preAD based on whole-brain. The correlation ρ value for the FDG SUVR/ReHo showed the highest area under curve of the preAD classification (0.787). The results imply the coupling relationship changed during the preAD stage in the DMN area.

Longitudinal Network Changes and Conversion to Cognitive Impairment in Multiple Sclerosis

OBJECTIVE

To characterize functional network changes related to conversion to cognitive impairment in a large sample of patients with multiple sclerosis (MS) over a period of 5 years.

METHODS

Two hundred twenty-seven patients with MS and 59 healthy controls of the Amsterdam MS cohort underwent neuropsychological testing and resting-state fMRI at 2 time points (time interval 4.9 ± 0.9 years). At both baseline and follow-up, patients were categorized as cognitively preserved (CP; n = 123), mildly impaired (MCI; z < -1.5 on ≥2 cognitive tests, n = 32), or impaired (CI; z < -2 on ≥2 tests, n = 72), and longitudinal conversion between groups was determined. Network function was quantified with eigenvector centrality, a measure of regional network importance, which was computed for individual resting-state networks at both time points.

RESULTS

Over time, 18.9% of patients converted to a worse phenotype; 22 of 123 patients who were CP (17.9%) converted from CP to MCI, 10 of 123 from CP to CI (8.1%), and 12 of 32 patients with MCI converted to CI (37.5%). At baseline, default-mode network (DMN) centrality was higher in CI individuals compared to controls (p = 0.05). Longitudinally, ventral attention network (VAN) importance increased in CP, driven by stable CP and CP-to-MCI converters (p < 0.05).

CONCLUSIONS

Of all patients, 19% worsened in their cognitive status over 5 years. Conversion from intact cognition to impairment is related to an initial disturbed functioning of the VAN, then shifting toward DMN dysfunction in CI. Because the VAN normally relays information to the DMN, these results could indicate that in MS normal processes crucial for maintaining overall network stability are progressively disrupted as patients clinically progress.

Anomalous functional connectivity within the default-mode network in treatment-naive patients possessing first-episode major depressive disorder

BACKGROUND AND OBJECTIVE

Previous studies have shown that the default-mode network (DMN) has a substantial role in patients with major depressive disorder (MDD). However, there is a shortage of information regarding variations in the functional connectivity (FC) of the DMN of treatment-naive patients with first-episode MDD. The present study aims to explore the FC of the DMN in such patients.

METHODS

The study population consisted of 33 patients and 35 controls, paired regarding age, gender, education level, and health condition. Depression severity was assessed through the Hamilton Depression Scale (HAM-D), and subjects underwent evaluation during the resting-state through functional magnetic resonance imaging (rs-fMRI). To assess the result, we used FC and ICA. We used Spearman's correlation test to detect potential correlations between anomalous FC and severity of HAM-D scores.

RESULTS

We have found a decreased FC in the left medial orbitofrontal gyrus (MOFG) and right marginal gyrus (SMG) in depressive patients compared to controls. There was a negative correlation between abnormal FC in the right SMG and HAM-D scores. We have not found any increase in FC of the DMN in treatment-naive, first-episode of MDD patients.

CONCLUSIONS

Our study provided evidence of a negative correlation between abnormal FC in the DMN and severity of depression symptoms measured by HAM-D in treatment-naive MDD patients. This finding could shed some light on the relevance of DMN for understanding the pathophysiology of cognitive impairment in MDD.

Mapping default mode connectivity alterations following a single season of subconcussive impact exposure in youth football

Repetitive head impact (RHI) exposure in collision sports may contribute to adverse neurological outcomes in former players. In contrast to a concussion, or mild traumatic brain injury, “subconcussive” RHIs represent a more frequent and asymptomatic form of exposure. The neural network‐level signatures characterizing subconcussive RHIs in youth collision‐sport cohorts such as American Football are not known. Here, we used resting‐state functional MRI to examine default mode network (DMN) functional connectivity (FC) following a single football season in youth players (n = 50, ages 8–14) without concussion. Football players demonstrated reduced FC across widespread DMN regions compared with non‐collision sport controls at postseason but not preseason. In a subsample from the original cohort (n = 17), players revealed a negative change in FC between preseason and postseason and a positive and compensatory change in FC during the offseason across the majority of DMN regions. Lastly, significant FC changes, including between preseason and postseason and between in‐ and off‐season, were specific to players at the upper end of the head impact frequency distribution. These findings represent initial evidence of network‐level FC abnormalities following repetitive, non‐concussive RHIs in youth football. Furthermore, the number of subconcussive RHIs proved to be a key factor influencing DMN FC.

Alterations in ventral attention network connectivity in individuals with prediabetes

Objective: Type 2 diabetes (T2D) is associated with aberrant neural functioning; however, the point at which brain function alterations occur in the progression of T2D is unknown. Here, we tested for differences in functional connectivity in adults with prediabetes and healthy individuals. We hypothesized that prediabetes, defined by glycated hemoglobin (HbA1c) 5.7-6.4% would be associated with disruptions in default mode network (DMN) connectivity. Methods: Fourteen brain networks were tested in 88 adults (prediabetes: n = 44; HbA1c = 5.8±0.2%; healthy: n = 44; HbA1c = 4.7±0.2%) matched for sex, age, and BMI. Results: We did not find differences in DMN connectivity between groups. Individuals with prediabetes showed stronger connectivity between the ventral attention network and (1) a visual network (p FWE = 0.0001); (2) a somatosensory network (p FWE = 0.0027). Individuals with healthy HbA1c showed stronger connectivity of the ventral attention network and (1) cingulo-opercular network (p FWE  =  0.002); (2) a thalamic-striatal-visual network (p FWE = 0.001). Conclusions: Relative to individuals with prediabetes, those with a healthy HbA1c showed stronger connectivity between brain networks underlying self-control and attention to stimuli. In contrast, those with prediabetes demonstrated stronger connectivity between brain networks associated with sensory and attention to stimuli. While T2D reported contribute to decreased DMN connectivity, prediabetes is characterized by a shift in functional connectivity from a self-control network towards increasing connectivity in sensory network.

Altered neural activities during response inhibition in adults with addiction: a voxel-wise meta-analysis

BACKGROUND

Previous literature has extensively investigated the brain activity during response inhibition in adults with addiction. Inconsistent results including both hyper- and hypo-activities in the fronto-parietal network (FPN) and the ventral attention network (VAN) have been found in adults with addictions, compared with healthy controls (HCs).

METHODS

Voxel-wise meta-analyses of abnormal task-evoked regional activity were conducted for adults with substance dependence (SD) and behavioral addiction during response inhibition tasks to solve previous inconsistencies. Twenty-three functional magnetic resonance imaging studies including 479 substance users, 38 individuals with behavioral addiction and 494 HCs were identified.

RESULTS

Compared with HCs, all addictions showed hypo-activities in regions within FPN (inferior frontal gyrus and supramarginal gyrus) and VAN (inferior frontal gyrus, middle temporal gyrus, temporal pole and insula), and hyper-activities in the cerebellum during response inhibition. SD subgroup showed almost the same activity patterns, with an additional hypoactivation of the precentral gyrus, compared with HCs. Stronger activation of the cerebellum was associated with longer addiction duration for adults with SD. We could not conduct meta-analytic investigations into the behavioral addiction subgroup due to the small number of datasets.

CONCLUSION

This meta-analysis revealed altered activation of FPN, VAN and the cerebellum in adults with addiction during response inhibition tasks using non-addiction-related stimuli. Although FPN and VAN showed lower activity, the cerebellum exhibited stronger activity. These results may help to understand the neural pathology of response inhibition in addiction.

Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.

A voxel-wise meta-analysis of task-based functional MRI studies on impaired gain and loss processing in adults with addiction

BACKGROUND

Disturbances in gain and loss processing have been extensively reported in adults with addiction, a brain disorder characterized by obsession with addictive substances or behaviours. Previous studies have provided conflicting results with respect to neural abnormalities in gain processing in addiction, and few investigations into loss processing.

METHODS

We conducted voxel-wise meta-analyses of abnormal task-evoked regional activities in adults with substance dependence and gambling addiction during the processing of gains and losses not related to their addiction (mainly monetary). We identified 24 studies, including 465 participants with substance dependence, 81 with gambling addiction and 490 healthy controls.

RESULTS

Compared with healthy controls, all participants with addictions showed hypoactivations in the prefrontal cortex, striatum and insula and hyperactivations in the default mode network during gain anticipation; hyperactivations in the prefrontal cortex and both hyper- and hypoactivations in the striatum during loss anticipation; and hyperactivations in the occipital lobe during gain outcome. In the substance dependence subgroup, activity in the occipital lobe was increased during gain anticipation but decreased during loss anticipation.

LIMITATIONS

We were unable to conduct meta-analyses in the gambling addiction subgroup because of a limited data set. We did not investigate the effects of clinical variables because of limited information.

CONCLUSION

The current study identified altered brain activity associated with higher- and lower-level function during gain and loss processing for non-addiction (mainly monetary) stimuli in adults with substance dependence and gambling addiction. Adults with addiction were more sensitive to anticipatory gains than losses at higher- and lower-level brain areas. These results may help us to better understand the pathology of gain and loss processing in addiction.

Autobiographical memory and default mode network function in schizophrenia: an fMRI study

BACKGROUND

The brain functional correlates of autobiographical recall are well established, but have been little studied in schizophrenia. Additionally, autobiographical memory is one of a small number of cognitive tasks that activates rather than de-activates the default mode network, which has been found to be dysfunctional in this disorder.

METHODS

Twenty-seven schizophrenic patients and 30 healthy controls underwent functional magnetic resonance imaging while viewing cue words that evoked autobiographical memories. Control conditions included both non-memory-evoking cues and a low level baseline (cross fixation).

RESULTS

Compared to both non-memory evoking cues and low level baseline, autobiographical recall was associated with activation in default mode network regions in the controls including the medial frontal cortex, the posterior cingulate cortex and the hippocampus, as well as other areas. Clusters of de-activation were seen outside the default mode network. There were no activation differences between the schizophrenic patients and the controls, but the patients showed clusters of failure of de-activation in non-default mode network regions.

CONCLUSIONS

According to this study, patients with schizophrenia show intact activation of the default mode network and other regions associated with recall of autobiographical memories. The finding of failure of de-activation outside the network suggests that schizophrenia may be associated with a general difficulty in de-activation rather than dysfunction of the default mode network per se.

Working memory training restores aberrant brain activity in adult attention-deficit hyperactivity disorder

The development of treatments for attention impairments is hampered by limited knowledge about the malleability of underlying neural functions. We conducted the first randomized controlled trial to determine the modulations of brain activity associated with working memory (WM) training in adults with attention-deficit hyperactivity disorder (ADHD). At baseline, we assessed the aberrant functional brain activity in the n-back WM task by comparing 44 adults with ADHD with 18 healthy controls using fMRI. Participants with ADHD were then randomized to train on an adaptive dual n-back task or an active control task. We tested whether WM training elicits redistribution of brain activity as observed in healthy controls, and whether it might further restore aberrant activity related to ADHD. As expected, activity in areas of the default-mode (DMN), salience (SN), sensory-motor (SMN), frontoparietal (FPN), and subcortical (SCN) networks was decreased in participants with ADHD at pretest as compared with healthy controls, especially when the cognitive load was high. WM training modulated widespread FPN and SN areas, restoring some of the aberrant activity. Training effects were mainly observed as decreased brain activity during the trained task and increased activity during the untrained task, suggesting different neural mechanisms for trained and transfer tasks.

Abnormalities of resting-state EEG in patients with prodromal and overt dementia with Lewy bodies: Relation to clinical symptoms

OBJECTIVE

Here we tested if cortical sources of resting state electroencephalographic (rsEEG) rhythms may differ in sub-groups of patients with prodromal and overt dementia with Lewy bodies (DLB) as a function of relevant clinical symptoms.

METHODS

We extracted clinical, demographic and rsEEG datasets in matched DLB patients (N = 60) and control Alzheimer's disease (AD, N = 60) and healthy elderly (Nold, N = 60) seniors from our international database. The eLORETA freeware was used to estimate cortical rsEEG sources.

RESULTS

As compared to the Nold group, the DLB and AD groups generally exhibited greater spatially distributed delta source activities (DLB > AD) and lower alpha source activities posteriorly (AD > DLB). As compared to the DLB "controls", the DLB patients with (1) rapid eye movement (REM) sleep behavior disorders showed lower central alpha source activities (p < 0.005); (2) greater cognitive deficits exhibited higher parietal and central theta source activities as well as higher central, parietal, and occipital alpha source activities (p < 0.01); (3) visual hallucinations pointed to greater parietal delta source activities (p < 0.005).

CONCLUSIONS

Relevant clinical features were associated with abnormalities in spatial and frequency features of rsEEG source activities in DLB patients.

SIGNIFICANCE

Those features may be used as neurophysiological surrogate endpoints of clinical symptoms in DLB patients in future cross-validation prospective studies.

More stringent criteria are needed for diagnosing internet gaming disorder: Evidence from regional brain features and whole-brain functional connectivity multivariate pattern analyses

BACKGROUND

Internet gaming disorder (IGD) is included in the DSM-5 as a provisional diagnosis. Whether IGD should be regarded as a disorder and, if so, how it should be defined and thresholded have generated considerable debate.

METHODS

In the current study, machine learning was used, based on regional and interregional brain features. Resting-state data from 374 subjects (including 148 IGD subjects with DSM-5 scores ≥5 and 93 IGD subjects with DSM-5 scores ≥6) were collected, and multivariate pattern analysis (MVPA) was employed to classify IGD from recreational game use (RGU) subjects based on regional brain features (ReHo) and communication between brain regions (functional connectivity; FC). Permutation tests were used to assess classifier performance.

RESULTS

The results demonstrated that when using DSM-5 scores ≥5 as the inclusion criteria for IGD subjects, MVPA could not differentiate IGD subjects from RGU, whether based on ReHo or FC features or by using different templates. MVPA could differentiate IGD subjects from RGU better than expected by chance when using DSM-5 scores ≥6 with both ReHo and FC features. The brain regions involved in the default mode network and executive control network and the cerebellum exhibited high discriminative power during classification.

DISCUSSION

The current findings challenge the current IGD diagnostic criteria thresholding proposed in the DSM-5, suggesting that more stringent criteria may be needed for diagnosing IGD. The findings suggest that brain regions involved in the default mode network and executive control network relate importantly to the core criteria for IGD.

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that have investigated functional brain alterations in AD; such contradictions have hindered efforts to elucidate the core disease mechanisms. In this study, we aim to comprehensively characterize AD-associated functional brain alterations using one of the world's largest resting-state functional MRI (fMRI) biobank for the disorder. The biobank includes fMRI data from six neuroimaging centers, with a total of 252 AD patients, 221 mild cognitive impairment (MCI) patients and 215 healthy comparison individuals. Meta-analytic techniques were used to unveil reliable differences in brain function among the three groups. Relative to the healthy comparison group, AD was associated with significantly reduced functional connectivity and local activity in the default-mode network, basal ganglia and cingulate gyrus, along with increased connectivity or local activity in the prefrontal lobe and hippocampus (p < .05, Bonferroni corrected). Moreover, these functional alterations were significantly correlated with the degree of cognitive impairment (AD and MCI groups) and amyloid-β burden. Machine learning models were trained to recognize key fMRI features to predict individual diagnostic status and clinical score. Leave-one-site-out cross-validation established that diagnostic status (mean area under the receiver operating characteristic curve: 0.85) and clinical score (mean correlation coefficient between predicted and actual Mini-Mental State Examination scores: 0.56, p < .0001) could be predicted with high accuracy. Collectively, our findings highlight the potential for a reproducible and generalizable functional brain imaging biomarker to aid the early diagnosis of AD and track its progression.

Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder

The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.

Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.

Evidence for default mode network dysfunction in borderline personality disorder

BACKGROUND

Although executive and other cognitive deficits have been found in patients with borderline personality disorder (BPD), whether these have brain functional correlates has been little studied. This study aimed to examine patterns of task-related activation and de-activation during the performance of a working memory task in patients with the disorder.

METHODS

Sixty-seven DSM-IV BPD patients and 67 healthy controls underwent fMRI during the performance of the n-back task. Linear models were used to obtain maps of within-group activations and areas of differential activation between the groups.

RESULTS

On corrected whole-brain analysis, there were no activation differences between the BPD patients and the healthy controls during the main 2-back v. baseline contrast, but reduced activation was seen in the precentral cortex bilaterally and the left inferior parietal cortex in the 2-back v. 1-back contrast. The patients showed failure of de-activation affecting the medial frontal cortex and the precuneus, plus in other areas. The changes did not appear to be attributable to previous history of depression, which was present in nearly half the sample.

CONCLUSIONS

In this study, there was some, though limited, evidence for lateral frontal hypoactivation in BPD during the performance of an executive task. BPD also appears to be associated with failure of de-activation in key regions of the default mode network.

Default mode dysfunction underpins suicidal activity in mood disorders

BACKGROUND

Suicide is a serious and not uncommon consequence of mood disorders that occurs primarily when individuals are depressed. Understanding the neurobiology of suicidal activity (thoughts or behaviors) is likely to facilitate prevention.

METHOD

Seventy-nine adult depressed mood disorder patients (MDP), of which 25 had attempted suicide at least once, and 66 healthy controls (HC) participated in this study. Resting-state functional MRI was used to identify neural activity differences between suicide attempters (SA) and non-attempters (NA). Specifically, differences were examined in functional connectivity both within and between four large cognitive networks [Executive Control (ECN), Default Mode (DMN), Salience (SN), and Basal Ganglia (BGN)] and their respective associations with suicidal activity.

RESULTS

Compared to HCs, patients had greater posterior DMN activity, but less activity in the BGN, and less low-frequency spectral power in the dorso-medial DMN. Furthermore, increased posterior DMN activity in SA was associated with recent suicidal activity, whereas NA had reduced BGN activity and less dorso-medial DMN spectral power, the latter being associated with lifelong suicidal thinking. SA also had greater activity in midline circuitry compared to both HC and NA, and the pattern of BGN and DMN co-activity differed between SA and NA.

CONCLUSIONS

DMN engagement raises the possibility that suicidal activity in mood disorder patients may be a consequence of impaired self-referential thought processing. Furthermore, differential BGN and DMN co-activation according to suicide attempt status suggests that attempting suicide perhaps alters cognitive flexibility. These insights are potentially useful for understanding the neural basis of suicide activity.

Large-scale dynamic causal modeling of major depressive disorder based on resting-state functional magnetic resonance imaging

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mostly task-based fMRI and incorporated only a few brain regions. To overcome these limitations and understand whether MDD is mediated by within-network or between-network connectivities, we applied spectral dynamic causal modeling to estimate EC of a large-scale network with 27 regions of interests from four distributed functional brain networks (default mode, executive control, salience, and limbic networks), based on large sample-size resting-state fMRI consisting of 100 healthy subjects and 100 individuals with first-episode drug-naive MDD. We applied a newly developed parametric empirical Bayes (PEB) framework to test specific hypotheses. We showed that MDD altered EC both within and between high-order functional networks. Specifically, MDD is associated with reduced excitatory connectivity mainly within the default mode network (DMN), and between the default mode and salience networks. In addition, the network-averaged inhibitory EC within the DMN was found to be significantly elevated in the MDD. The coexistence of the reduced excitatory but increased inhibitory causal connections within the DMNs may underlie disrupted self-recognition and emotional control in MDD. Overall, this study emphasizes that MDD could be associated with altered causal interactions among high-order brain functional networks.

Functional dedifferentiation of associative resting state networks in older adults - A longitudinal study

Healthy aging is associated with weaker functional connectivity within resting state brain networks and stronger functional interaction between these networks. This phenomenon has been characterized as reduced functional segregation and has been investigated mainly in cross-sectional studies. Here, we used a longitudinal dataset which consisted of four occasions of resting state fMRI and psychometric cognitive ability data, collected from a sample of healthy older adults (baseline N = 232, age range: 64-87 y, age M = 70.8 y), to investigate the functional segregation of several well-defined resting state networks encompassing the whole brain. We characterized the ratio of within-network and between-network correlations via the well-established segregation index. Our findings showed a decrease over a 4-year interval in the functional segregation of the default mode, frontoparietal control and salience ventral attention networks. In contrast, we showed an increase in the segregation of the limbic network over the same interval. More importantly, the rate of change in functional segregation of the frontoparietal control network was associated with the rate of change in processing speed. These findings support the hypothesis of functional dedifferentiation in healthy aging as well as its role in cognitive function in elderly.

Brain connectivity and cognitive functioning in individuals six months after multiorgan failure

Multiorgan failure (MOF) is a life-threating condition that affects two or more systems of organs not involved in the disorder that motivates admission to an Intensive Care Unit (ICU). Patients who survive MOF frequently present long-term functional, neurological, cognitive, and psychiatric sequelae. However, the changes to the brain that explain such symptoms remain unclear.

OBJECTIVE

To determine brain connectivity and cognitive functioning differences between a group of MOF patients six months after ICU discharge and healthy controls (HC).

METHODS

22 MOF patients and 22 HC matched by age, sex, and years of education were recruited. Both groups were administered a 3T magnetic resonance imaging (MRI), including structural T1 and functional BOLD, as well as a comprehensive neuropsychological evaluation that included tests of learning and memory, speed of information processing and attention, executive function, visual constructional abilities, and language. Voxel-based morphometry was used to analyses T1 images. For the functional data at rest, functional connectivity (FC) analyses were performed.

RESULTS

There were no significant differences in structural imaging and neuropsychological performance between groups, even though patients with MOF performed worse in all the cognitive tests. Functional neuroimaging in the default mode network (DMN) showed hyper-connectivity towards sensory-motor, cerebellum, and visual networks. DMN connectivity had a significant association with the severity of MOF during ICU stay and with the neuropsychological scores in tests of attention and visual constructional abilities.

CONCLUSIONS

In MOF patients without structural brain injury, DMN connectivity six months after ICU discharge is associated with MOF severity and neuropsychological impairment, which supports the use of resting-state functional MRI as a potential tool to predict the onset of long-term cognitive deficits in these patients. Similar to what occurs at the onset of other pathologies, the observed hyper-connectivity might suggest network re-adaptation following MOF.

Default mode network and the timed up and go in MCI: A structural covariance analysis

BACKGROUND

The timed up and go (TUG) is a test used to assess mobility in older adults and patients with neurological conditions. This study aims to compare brain gray matter (GM) correlates and structural covariance networks associated with the TUG time in cognitively healthy individuals (CHI) and in patients with mild cognitive impairment (MCI).

METHODS

The TUG time was measured in 326 non-demented older community-dwellers (age 71.3 ± 4.5; 42% female) - 156 CHI and 170 MCI. GM covariance networks were computed using voxel-based morphometry with the main neural correlates of TUG for each group as seed regions.

RESULTS

Increased TUG time (i.e., poor performance) was associated with distinct brain volume reductions between CHI and MCI. The covariance analysis showed cortical regions involving the default mode network in CHI and bilateral cerebellar regions in MCI.

CONCLUSIONS

GM networks associated with the TUG vary between CHI and MCI, suggesting distinct brain control for locomotion between CHI and MCI patients.