Global connectivity of the fronto-parietal cognitive control network is related to depression symptoms in the general population

Insomnia and stress: the mediating roles of frontoparietal network

Insomnia is a widespread health problem among adults, and it impairs cognitive control and emotional regulation functions. Stress and insomnia are positively correlated, and their vicious cycle has been widely reported. In this study, we explore the neural biomarkers of insomnia from the perspective of whole-brain functional connectivity and investigate the neural mechanisms underlying the association between stress and insomnia. The current study was conducted on a cross-sectional sample (N = 430). First, we investigated the correlation between perceived stress and insomnia. Second, we applied connectome-based predictive modeling (CPM) to determine the neuromarkers of insomnia. Finally, we explored the neural basis underlying the association between perceived stress and insomnia. A significant positive correlation was found between perceived stress and insomnia in the present research. Results of CPM revealed the following as the neural substrates supporting insomnia: the emotion regulation circuit involving repetitive negative thinking and the cognitive control circuit involving attention control. According to further results from mediation analysis, the frontoparietal network supporting cognitive emotion regulation is an important neural mechanism that maintains the correlation between stress and insomnia. The present study offers a profound insight into the alterations of brain activity related to insomnia, and it further investigates the neural underpinnings of the robust association between stress and insomnia. This study also opens new avenues for neural interventions to alleviate stress-related insomnia.

Differences in Children and Adolescents with Depression before and after a Remediation Program: An Event-Related Potential Study

Depression is clinically diagnosed when a defined constellation of symptoms manifests over a specific duration with notable severity. According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), Major Depressive Disorder (MDD) is characterized by the presence of five or more symptoms persisting for at least two weeks. As a profound mental health condition affecting millions globally, depression presents a considerable challenge for researchers and clinicians alike. In pediatric and adolescent populations, depression can precipitate adverse outcomes, including substance abuse, academic difficulties, risky sexual behaviors, physical health problems, impaired social relationships, and a markedly elevated risk of suicide-up to thirty times higher than the general population. This paper details a study that evaluated the efficacy of Cognitive Behavioral Therapy (CBT) alone vs. CBT combined with selective serotonin reuptake inhibitors (SSRIs) in a treatment program. The study cohort comprised sixteen (16) children and adolescents diagnosed with depression (eight males and eight females) and sixteen (16) typically developing peers (eight males and eight females) aged from 9 to 15 years (Mean age = 11.94, standard deviation = 2.02). Initial assessments employed Event-Related Potentials (ERPs), the Children's Depression Inventory (CDI), and reaction time measurements. The results reveal that participants with depression exhibit cognitive deficits in attention and memory, as evidenced by prolonged P300 latencies. Following intervention with either CBT alone or CBT combined with medication, the depressed participants demonstrated significant improvements, evidenced by lower CDI scores, reduced P300 latencies, and faster reaction times, both compared to their pre-treatment status and relative to the control group.

Annual Research Review: Neuroimmune network model of depression: a developmental perspective

Depression is a serious public health problem, and adolescence is an 'age of risk' for the onset of Major Depressive Disorder. Recently, we and others have proposed neuroimmune network models that highlight bidirectional communication between the brain and the immune system in both mental and physical health, including depression. These models draw on research indicating that the cellular actors (particularly monocytes) and signaling molecules (particularly cytokines) that orchestrate inflammation in the periphery can directly modulate the structure and function of the brain. In the brain, inflammatory activity heightens sensitivity to threats in the cortico-amygdala circuit, lowers sensitivity to rewards in the cortico-striatal circuit, and alters executive control and emotion regulation in the prefrontal cortex. When dysregulated, and particularly under conditions of chronic stress, inflammation can generate feelings of dysphoria, distress, and anhedonia. This is proposed to initiate unhealthy, self-medicating behaviors (e.g. substance use, poor diet) to manage the dysphoria, which further heighten inflammation. Over time, dysregulation in these brain circuits and the inflammatory response may compound each other to form a positive feedback loop, whereby dysregulation in one organ system exacerbates the other. We and others suggest that this neuroimmune dysregulation is a dynamic joint vulnerability for depression, particularly during adolescence. We have three goals for the present paper. First, we extend neuroimmune network models of mental and physical health to generate a developmental framework of risk for the onset of depression during adolescence. Second, we examine how a neuroimmune network perspective can help explain the high rates of comorbidity between depression and other psychiatric disorders across development, and multimorbidity between depression and stress-related medical illnesses. Finally, we consider how identifying neuroimmune pathways to depression can facilitate a 'next generation' of behavioral and biological interventions that target neuroimmune signaling to treat, and ideally prevent, depression in youth and adolescents.

Concussion-Related Disruptions to Hub Connectivity in the Default Mode Network Are Related to Symptoms and Cognition

Concussions present with a myriad of symptomatic and cognitive concerns; however, the relationship between these functional disruptions and the underlying changes in the brain are not yet well understood. Hubs, or brain regions that are connected to many different functional networks, may be specifically disrupted after concussion. Given the implications in concussion research, we quantified hub disruption within the default mode network (DMN) and between the DMN and other brain networks. We collected resting-state functional magnetic resonance imaging data from collegiate student-athletes (n = 44) at three time points: baseline (before beginning their athletic season), acute post-injury (approximately 48h after a diagnosed concussion), and recovery (after starting return-to-play progression, but before returning to contact). We used self-reported symptoms and computerized cognitive assessments collected across similar time points to link these functional connectivity changes to clinical outcomes. Concussion resulted in increased connectivity between regions within the DMN compared with baseline and recovery, and this post-injury connectivity was more positively related to symptoms and more negatively related to visual memory performance compared with baseline and recovery. Further, concussion led to decreased connectivity between DMN hubs and visual network non-hubs relative to baseline and recovery, and this post-injury connectivity was more negatively related to somatic symptoms and more positively related to visual memory performance compared with baseline and recovery. Relationships between functional connectivity, symptoms, and cognition were not significantly different at baseline versus recovery. These results highlight a unique relationship between self-reported symptoms, visual memory performance, and acute functional connectivity changes involving DMN hubs after concussion in athletes. This may provide evidence for a disrupted balance of within- and between-network communication highlighting possible network inefficiencies after concussion. These results aid in our understanding of the pathophysiological disruptions after concussion and inform our understanding of the associations between disruptions in brain connectivity and specific clinical presentations acutely post-injury.

EEG biomarkers of behavioral inhibition in patients with depression who committed violent offenses: a Go/NoGo ERP study

Understanding the neurobiological correlates of behavioral inhibition in patients with depression who committed violent offenses could contribute to the prediction and prevention of violence. The present study recruited 29 depressed patients with violent offenses (VD group), 27 depressed patients without violent behavior (NVD group), and 28 healthy controls (HC group) to complete a visual Go/NoGo task, during which their responses and electroencephalography were simultaneously recorded using an event-related potentiometer. The results showed that the VD group made more commission errors and responded more slowly relative to the NVD and HC groups. The P3 amplitude of the VD group was reduced in the frontal and central brain regions compared to the HC group and increased in the parietal regions compared to the NVD group. In comparison to Go stimuli, NoGo stimuli induced longer P3 latencies in frontal regions in both the VD and NVD groups; however, this difference was not statistically significant in the HC group. These results provide electrophysical evidence of behavioral inhibition deficits in patients with depression, especially in those with violent behaviors. The reduced P3 amplitude in the frontal-central regions, increased P3 amplitude in the parietal regions, and increased NoGo P3 latency may be potential electrophysiological features that can predict violent behavior in patients with depression.

Mild Behavioral Impairment in Parkinson's Disease: An Updated Review on the Clinical, Genetic, Neuroanatomical, and Pathophysiological Aspects

Neuropsychiatric symptoms (NPS), including depression, anxiety, apathy, visual hallucinations, and impulse control disorders, are very common during the course of Parkinson's disease (PD), occurring even at the prodromal and premotor stages. Mild behavioral impairment (MBI) represents a recently described neurobehavioral syndrome, characterized by the emergence of persistent and impactful NPS in later life, reflecting arisk of dementia. Accumulating evidence suggests that MBI is highly prevalent in non-demented patients with PD, also being associated with an advanced disease stage, more severe motor deficits, as well as global and multiple-domain cognitive impairment. Neuroimaging studies have revealed that MBI in patients with PD may be related todistinct patterns of brain atrophy, altered neuronal connectivity, and distribution of dopamine transporter (DAT) depletion, shedding more light on its pathophysiological background. Genetic studies in PD patients have also shown that specific single-nucleotide polymorphisms (SNPs) may be associated with MBI, paving the way for future research in this field. In this review, we summarize and critically discuss the emerging evidence on the frequency, associated clinical and genetic factors, as well as neuroanatomical and neurophysiological correlates of MBI in PD, aiming to elucidate the underlying pathophysiology and its potential role as an early "marker" of cognitive decline, particularly in this population. In addition, we aim to identify research gaps, and propose novel relative areas of interest that could aid in our better understanding of the relationship of this newly defined diagnostic entity with PD.

Maternal prenatal distress exposure negatively associates with the stability of neonatal frontoparietal network

Maternal prenatal distress (PD), frequently defined as in utero prenatal stress exposure (PSE) to the developing fetus, influences the developing brain and numerous associations between PSE and brain structure have been described both in neonates and in older children. Previous studies addressing PSE-linked alterations in neonates' brain activity have focused on connectivity analyses from predefined seed regions, but the effects of PSE at the level of distributed functional networks remains unclear. In this study, we investigated the impact of prenatal distress on the spatial and temporal properties of functional networks detected in functional MRI data from 20 naturally sleeping, term-born (age 25.85 ± 7.72 days, 11 males), healthy neonates. First, we performed group level independent component analysis (GICA) to evaluate an association between PD and the identified functional networks. Second, we searched for an association with PD at the level of the stability of functional networks over time using leading eigenvector dynamics analysis (LEiDA). No statistically significant associations were detected at the spatial level for the GICA-derived networks. However, at the dynamic level, LEiDA revealed that maternal PD negatively associated with the stability of a frontoparietal network. These results imply that maternal PD may influence the stability of frontoparietal connections in neonatal brain network dynamics and adds to the cumulating evidence that frontal areas are especially sensitive to PSE. We advocate for early preventive intervention strategies regarding pregnant mothers. Nevertheless, future research venues are required to assess optimal intervention timing and methods for maximum benefit.

EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.

Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments

One of the most critical challenges in using noninvasive brain stimulation (NIBS) techniques for the treatment of psychiatric and neurologic disorders is inter- and intra-individual variability in response to NIBS. Response variations in previous findings suggest that the one-size-fits-all approach does not seem the most appropriate option for enhancing stimulation outcomes. While there is a growing body of evidence for the feasibility and effectiveness of individualized NIBS approaches, the optimal way to achieve this is yet to be determined. Transcranial electrical stimulation (tES) is one of the NIBS techniques showing promising results in modulating treatment outcomes in several psychiatric and neurologic disorders, but it faces the same challenge for individual optimization. With new computational and methodological advances, tES can be integrated with real-time functional magnetic resonance imaging (rtfMRI) to establish closed-loop tES-fMRI for individually optimized neuromodulation. Closed-loop tES-fMRI systems aim to optimize stimulation parameters based on minimizing differences between the model of the current brain state and the desired value to maximize the expected clinical outcome. The methodological space to optimize closed-loop tES fMRI for clinical applications includes (1) stimulation vs. data acquisition timing, (2) fMRI context (task-based or resting-state), (3) inherent brain oscillations, (4) dose-response function, (5) brain target trait and state and (6) optimization algorithm. Closed-loop tES-fMRI technology has several advantages over non-individualized or open-loop systems to reshape the future of neuromodulation with objective optimization in a clinically relevant context such as drug cue reactivity for substance use disorder considering both inter and intra-individual variations. Using multi-level brain and behavior measures as input and desired outcomes to individualize stimulation parameters provides a framework for designing personalized tES protocols in precision psychiatry.

Predicting Dimensional Antidepressant Response to Repetitive Transcranial Magnetic Stimulation using Pretreatment Resting-state Functional Connectivity

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression and has been shown to modulate resting-state functional connectivity (RSFC) of depression-relevant neural circuits. To date, however, few studies have investigated whether individual treatment-related symptom changes are predictable from pretreatment RSFC. We use machine learning to predict dimensional changes in depressive symptoms using pretreatment patterns of RSFC. We hypothesized that changes in dimensional depressive symptoms would be predicted more accurately than scale total scores. Patients with depression (n=26) underwent pretreatment RSFC MRI. Depressive symptoms were assessed with the 17-item Hamilton Depression Rating Scale (HDRS-17). Random forest regression (RFR) models were trained and tested to predict treatment-related symptom changes captured by the HDRS-17, HDRS-6 and three previously identified HDRS subscales: core mood/anhedonia (CMA), somatic disturbances, and insomnia. Changes along the CMA, HDRS-17, and HDRS-6 were predicted significantly above chance, with 9%, 2%, and 2% of out-of-sample outcome variance explained, respectively (all p<0.01). CMA changes were predicted more accurately than the HDRS-17 (p<0.05). Higher baseline global connectivity (GC) of default mode network (DMN) subregions and the somatomotor network (SMN) predicted poorer symptom reduction, while higher GC of the right dorsal attention (DAN) frontoparietal control (FPCN), and visual networks (VN) predicted reduced CMA symptoms. HDRS-17 and HDRS-6 changes were predicted with similar GC patterns. These results suggest that RSFC spanning the DMN, SMN, DAN, FPCN, and VN subregions predict dimensional changes with greater accuracy than syndromal changes following rTMS. These findings highlight the need to assess more granular clinical dimensions in therapeutic studies, particularly device neuromodulation studies, and echo earlier studies supporting that dimensional outcomes improve model accuracy.

Metabolic Covariance Connectivity of Posterior Cingulate Cortex Associated with Depression Symptomatology Level in Healthy Young Adults

Early detection in the development of a Major Depressive Disorder (MDD) could guide earlier clinical interventions. Although MDD can begin at a younger age, most people have their first episode in young adulthood. The underlying pathophysiological mechanisms relating to such an increased risk are not clear. The posterior cingulate cortex (PCC), exhibiting high levels of brain connectivity and metabolic activity, plays a pivotal role in the pathological mechanism underlying MDD. In the current study, we used the (F-18) fluorodeoxyglucose (FDG) positron emission tomography (PET) to measure metabolic covariance connectivity of the PCC and investigated its association with depression symptomatology evaluated by the Centre for Epidemiological Studies Depression Inventory-Revised (CESD-R) among 27 healthy individuals aged between 18 and 23 years. A significant negative correlation has been observed between CESD-R scale scores and the PCC metabolic connectivity with the anterior cingulate, medial prefrontal cortex, inferior and middle frontal gyrus, as well as the insula. Overall, our findings suggest that the neural correlates of depressive symptomatology in healthy young adults without a formal diagnosis involve the metabolic connectivity of the PCC. Our findings may have potential implications for early identification and intervention in people at risk of developing depression.

Linking individual variability in functional brain connectivity to polygenic risk in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disease, which brings great difficulties to clinical diagnosis and therapy. Its mechanism is still unknown. Prior neuroimaging studies mainly focused on mean differences between patients and healthy controls (HC), largely ignoring individual differences between patients.

METHODS

This study included 112 MDD patients and 93 HC subjects. Resting-state functional MRI data were obtained to examine the patterns of individual variability of brain functional connectivity (IVFC). The genetic risk of pathways including dopamine, 5-hydroxytryptamine (5-HT), norepinephrine (NE), hypothalamic-pituitary-adrenal (HPA) axis, and synaptic plasticity was assessed by multilocus genetic profile scores (MGPS), respectively.

RESULTS

The IVFC pattern of the MDD group was similar but higher than that in HCs. The inter-network functional connectivity in the default mode network contributed to altered IVFC in MDD. 5-HT, NE, and HPA pathway genes affected IVFC in MDD patients. The age of onset, duration, severity, and treatment response, were correlated with IVFC. IVFC in the left ventromedial prefrontal cortex had a mediating effect between MGPS of the 5-HT pathway and baseline depression severity.

LIMITATIONS

Environmental factors and differences in locations of functional areas across individuals were not taken into account.

CONCLUSIONS

This study found MDD patients had significantly different inter-individual functional connectivity variations than healthy people, and genetic risk might affect clinical manifestations through brain function heterogeneity.

Emotional Dysregulation in Mild Behavioral Impairment Is Associated with Reduced Cortical Thickness in the Right Supramarginal Gyrus

BACKGROUND

Mild behavioral impairment (MBI) has attracted attention as a possible precursor symptom of dementia, but its neural basis has not been fully investigated.

OBJECTIVE

We aimed to investigate the relationship between MBI and surface area, cortical thickness, and volume in the temporal and parietal lobes, which are strongly associated with dementia and emotional disorders.

METHODS

This retrospective study evaluated 123 participants: 90 with mild cognitive impairment (MCI), 13 with subjective cognitive decline (SCD), and 20 cognitively healthy (CH). Using analysis of covariance (ANCOVA) with sex, age, and MMSE score as covariates, cortical thickness, surface area, and volume in 10 regions were compared between groups with and without MBI. Groups with MBI emotional dysregulation were also compared with groups without MBI.

RESULTS

ANCOVA revealed significantly smaller cortical thickness in the MBI group's right parahippocampal (p = 0.01) and supramarginal gyri (p = 0.002). After multiple comparison correction, only the right supramarginal gyrus was significantly smaller (p = 0.02). When considering only MBI emotional dysregulation, the right parahippocampal and supramarginal gyrus' cortical thicknesses were significantly smaller in this MBI group (p = 0.03, 0.01). However, multiple comparison correction identified no significant differences (p = 0.14, 0.11).

CONCLUSION

Overall MBI and the emotional dysregulation domains were associated with reduced cortical thickness in the right parahippocampal and supramarginal gyri. Since neurodegeneration in the medial temporal and parietal lobe precedes early Alzheimer's disease (AD), MBI, particularly emotion dysregulation, may predict early AD below the diagnostic threshold.

Transcranial Photobiomodulation Treatment: Significant Improvements in Four Ex-Football Players with Possible Chronic Traumatic Encephalopathy

Background

Chronic traumatic encephalopathy, diagnosed postmortem (hyperphosphorylated tau), is preceded by traumatic encephalopathy syndrome with worsening cognition and behavior/mood disturbances, over years. Transcranial photobiomodulation (tPBM) may promote improvements by increasing ATP in compromised/stressed cells and increasing local blood, lymphatic vessel vasodilation.

Objective

Aim 1: Examine cognition, behavior/mood changes Post-tPBM. Aim 2: MRI changes - resting-state functional-connectivity MRI: salience, central executive, default mode networks (SN, CEN, DMN); magnetic resonance spectroscopy, cingulate cortex.

Methods

Four ex-players with traumatic encephalopathy syndrome/possible chronic traumatic encephalopathy, playing 11- 16 years, received In-office, red/near-infrared tPBM to scalp, 3x/week for 6 weeks. Two had cavum septum pellucidum.

Results

The three younger cases (ages 55, 57, 65) improved 2 SD (p < 0.05) on three to six neuropsychological tests/subtests at 1 week or 1 month Post-tPBM, compared to Pre-Treatment, while the older case (age 74) improved by 1.5 SD on three tests. There was significant improvement at 1 month on post-traumatic stress disorder (PTSD), depression, pain, and sleep. One case discontinued narcotic pain medications and had reduced tinnitus. The possible placebo effect is unknown. At 2 months Post-tPBM, two cases regressed. Then, home tPBM was applied to only cortical nodes, DMN (12 weeks); again, significant improvements were seen. Significant correlations for increased SN functional connectivity (FC) over time, with executive function, attention, PTSD, pain, and sleep; and CEN FC, with verbal learning/memory, depression. Increased n-acetyl-aspartate (NAA) (oxygen consumption, mitochondria) was present in anterior cingulate cortex (ACC), parallel to less pain and PTSD.

Conclusion

After tPBM, these ex-football players improved. Significant correlations of increased SN FC and CEN FC with specific cognitive tests and behavior/mood ratings, plus increased NAA in ACC support beneficial effects from tPBM.

Dorsal attention network activity during perceptual organization is distinct in schizophrenia and predictive of cognitive disorganization

Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes, but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether brain network differences in schizophrenia occur in related illnesses or vary with illness features transdiagnostically. To address these topics, we scanned (functional magnetic resonance imaging, fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ('modulations') in the dorsal attention network (DAN) could distinguish people with schizophrenia from the other groups (AUCs > .85) and could transdiagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except in schizophrenia. The secondary visual network was strongly and similarly modulated in each group. Task modulations were dispersed over more networks in patients compared to controls. In summary, DAN activity during visual perceptual organization is distinct in schizophrenia, symptomatically relevant, and potentially related to improper attention-related feedback into secondary visual areas.

Disrupted topological organization of functional brain networks in Alzheimer's disease patients with depressive symptoms

BACKGROUND

Depression is a common symptom of Alzheimer's disease (AD), but the underlying neural mechanism is unknown. The aim of this study was to explore the topological properties of AD patients with depressive symptoms (D-AD) using graph theoretical analysis.

METHODS

We obtained 3-Tesla rsfMRI data from 24 D-AD patients, 20 non-depressed AD patients (nD-AD), and 20 normal controls (NC). Resting state networks were identified using graph theory analysis. ANOVA with a two-sample t-test post hoc analysis in GRETNA was used to assess the topological measurements.

RESULTS

Our results demonstrate that the three groups show characteristic properties of a small-world network. NCs showed significantly larger global and local efficiency than D-AD and nD-AD patients. Compared with nD-AD patients, D-AD patients showed decreased nodal centrality in the pallidum, putamen, and right superior temporal gyrus. They also showed increased nodal centrality in the right superior parietal gyrus, the medial orbital portion of the right superior frontal gyrus, and the orbital portion of the right superior frontal gyrus. Compared with nD-AD patients, NC showed decreased nodal betweenness in the right superior temporal gyrus, and increased nodal betweenness in medial orbital part of the right superior frontal gyrus.

CONCLUSIONS

These results indicate that D-AD is associated with alterations of topological structure. Our study provides new insights into the brain mechanisms underlying D-AD.

Beyond noise to function: reframing the global brain activity and its dynamic topography

How global and local activity interact with each other is a common question in complex systems like climate and economy. Analogously, the brain too displays 'global' activity that interacts with local-regional activity and modulates behavior. The brain's global activity, investigated as global signal in fMRI, so far, has mainly been conceived as non-neuronal noise. We here review the findings from healthy and clinical populations to demonstrate the neural basis and functions of global signal to brain and behavior. We show that global signal (i) is closely coupled with physiological signals and modulates the arousal level; and (ii) organizes an elaborated dynamic topography and coordinates the different forms of cognition. We also postulate a Dual-Layer Model including both background and surface layers. Together, the latest evidence strongly suggests the need to go beyond the view of global signal as noise by embracing a dual-layer model with background and surface layer.

Global connectivity fingerprints predict the domain generality of multiple-demand regions

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

Earlier Alzheimer’s disease onset is associated with tau pathology in brain hub regions and facilitated tau spreading

In Alzheimer’s disease (AD), younger symptom onset is associated with accelerated disease progression and tau spreading, yet the mechanisms underlying faster disease manifestation are unknown. To address this, we combined resting-state fMRI and longitudinal tau-PET in two independent samples of controls and biomarker-confirmed AD patients (ADNI/BioFINDER, n = 240/57). Consistent across both samples, we found that younger symptomatic AD patients showed stronger tau-PET in globally connected fronto-parietal hubs, i.e., regions that are critical for maintaining cognition in AD. Stronger tau-PET in hubs predicted faster subsequent tau accumulation, suggesting that tau in globally connected regions facilitates connectivity-mediated tau spreading. Further, stronger tau-PET in hubs mediated the association between younger age and faster tau accumulation in symptomatic AD patients, which predicted faster cognitive decline. These independently validated findings suggest that younger AD symptom onset is associated with stronger tau pathology in brain hubs, and accelerated tau spreading throughout connected brain regions and cognitive decline.

Effects of escitalopram therapy on functional brain controllability in major depressive disorder

Antidepressant drugs are the mainstay of treatment for patients with major depressive disorders (MDD). Given the critical role of the underlying neural control mechanism in the physiopathology of depression, this study aims to investigate the effects of escitalopram, a type of antidepressant drug, on the changes of functional brain controllability throughout the escitalopram treatment for MDD. We collected resting-state functional magnetic resonance imaging data from 20 unmedicated major depressive patients at baseline (visit 1, pre-treatment), one week (visit 2, 1-week after the onset of the treatment) and six weeks (visit 3, after the 6-week escitalopram treatment). Our results revealed that the global average and modal controllability of MDD patients were significantly larger and smaller, respectively, compared to healthy subjects (P < 0.01). Furthermore, the modal controllability rank of the frontoparietal network in depression patients was also significantly smaller than the healthy subjects (P < 0.01). However, throughout the escitalopram treatment, the global average and modal controllability, and the controllability of the default mode network and frontoparietal network of MDD patients were consistently changed to the healthy subjects' level. Our results also showed that the changes of global average and modal controllability measures can predict the improvements of clinical scores of the MDD patients as the escitalopram treatment advanced (P < 0.05). In conclusion, this study reveals promising brain controllability-based biomarkers to mechanistically understand and predict the effects of the escitalopram treatment for depression and maybe extended to predict and understand the effects of other interventions for other neurological and psychiatric diseases.

Sustained attention alterations in major depressive disorder: A review of fMRI studies employing Go/No-Go and CPT tasks

BACKGROUND

Major depressive disorder (MDD) is a severe psychiatric condition characterized by selective cognitive dysfunctions. In this regard, functional Magnetic Resonance Imaging (fMRI) studies showed, both at resting state and during tasks, alterations in the brain functional networks involved in cognitive processes in MDD patients compared to controls. Among those, it seems that the attention network may have a role in the disease pathophysiology. Therefore, in this review we aim at summarizing the current fMRI evidence investigating sustained attention in MDD patients.

METHODS

We conducted a search on PubMed on case-control studies on MDD employing fMRI acquisitions during Go/No-Go and continuous performance tasks. A total of 12 studies have been included in the review.

RESULTS

Overall, the majority of fMRI studies reported quantitative alterations in the response to attentive tasks in selective brain regions, including the prefrontal cortex, the cingulate cortex, the temporal and parietal lobes, the insula and the precuneus, which are key nodes of the attention, the executive, and the default mode networks.

LIMITATIONS

The heterogeneity in the study designs, fMRI acquisition techniques and processing methods have limited the generalizability of the results.

CONCLUSIONS

The results from the included studies showed the presence of alterations in the activation patterns of regions involved in sustained attention in MDD, which are in line with current evidence and seemed to explain some of the key symptoms of depression. However, given the paucity and heterogeneity of studies available, it may be worthwhile to continue investigating the attentional domain in MDD with ad-hoc study designs to retrieve more robust evidence.

Brain pathology and cognitive scores prior to onset of late-life depression

OBJECTIVES

Understanding the biological changes that occur prior to onset of late-life depression (LLD) is key to its prevention. To investigate potential predictors of LLD, we assessed cognitive scores and neurodegenerative and vascular biomarkers in healthy older adults who later developed depression.

METHODS

Longitudinal data from the Alzheimer's Disease Neuroimaging Initiative of 241 cognitively unimpaired and non-depressed older adults aged 56-90 at baseline with at least 4 years of follow-up were included. Participants were classified based on whether they developed an incident depression (n = 96) or not (n = 145). Cognitive measures of memory, executive functioning, and language, and biomarkers proposed to be related to LLD: hippocampal volume, white matter hyperintensity volume (WMH), and cortical and cerebrospinal fluid (CSF) amyloid beta levels, were compared between the incident depression and the never-depressed groups at four time points: at baseline, the visit prior to onset, at onset, and after the onset of depression.

RESULTS

In the incident depression group, there was a mild decline in cognitive scores from baseline to the visit before depression onset compared with the never-depressed group. The cognitive differences between the groups became more marked after depression onset. Baseline cortical amyloid burden, CSF amyloid beta levels, and WMH were significant predictors of incident depression. Compared to the non-depressed group, hippocampal volume was not reduced before onset, but was reduced following depression.

CONCLUSIONS

Amyloid pathology and WMH can predict future development of LLD in cognitively unimpaired individuals and may be involved in precipitating vulnerability for depression in older adults.

Neural Correlates of Negative Emotion Processing in Subthreshold Depression

Subthreshold depression (SD) is regarded as a major risk factor for major depression. However, little is known about the neural mechanism of negative emotion processing in SD. The study aimed to examine the differentiate neural correlates for negative emotion processing in SD and health controls (HC) and to investigate changes in functional connectivity in SD compared with HC. BOLD responses of SD and HC were captured while performing a passive viewing task, which comprised a negative condition and a masked condition. A total of 42 SD and 32 HC adolescents participated the study. Between-group comparisons revealed significant reduced activations in the superior frontal gyrus (SFG), middle frontal gyrus (MFG), and middle cingulate gyrus. Region of interest (ROI) analyses did not find correlations between contrast values of the ROIs and depressive symptoms. In addition, we found significant increased functional connectivity between the SFG and caudate, pallidum, and insula, which were significantly correlated with depressive symptoms in the SD group (P &lt; 0.05). Altered functional connectivity between the SFG and caudate, pallidum, and insula may underlie the pathology of SD. This is the first study to investigate neural mechanisms of negative emotion processing in SD using task-based fMRI.

Constructing neural network models from brain data reveals representational transformations linked to adaptive behavior

The human ability to adaptively implement a wide variety of tasks is thought to emerge from the dynamic transformation of cognitive information. We hypothesized that these transformations are implemented via conjunctive activations in "conjunction hubs"-brain regions that selectively integrate sensory, cognitive, and motor activations. We used recent advances in using functional connectivity to map the flow of activity between brain regions to construct a task-performing neural network model from fMRI data during a cognitive control task. We verified the importance of conjunction hubs in cognitive computations by simulating neural activity flow over this empirically-estimated functional connectivity model. These empirically-specified simulations produced above-chance task performance (motor responses) by integrating sensory and task rule activations in conjunction hubs. These findings reveal the role of conjunction hubs in supporting flexible cognitive computations, while demonstrating the feasibility of using empirically-estimated neural network models to gain insight into cognitive computations in the human brain.

Discriminating subclinical depression from major depression using multi-scale brain functional features: A radiomics analysis

BACKGROUND

The diagnosis of subclinical depression (SD) currently relies exclusively on subjective clinical scores and structured interviews, which shares great similarities with major depression (MD) and increases the risk of misdiagnosis of SD and MD. This study aimed to develop a method of disease classification for SD and MD by resting-state functional features using radiomics strategy.

METHODS

Twenty-six SD, 36 MD subjects and 33 well-matched healthy controls (HC) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). A novel radiomics analysis was proposed to discriminate SD from MD. Multi-scale brain functional features were extracted to explore a comprehensive representation of functional characteristics. A two-level feature selection strategy and support vector machine (SVM) were employed for classification.

RESULTS

The overall classification accuracy among SD, MD and HC groups was 84.21%. Particularly, the model excellently distinguished SD from MD with 96.77% accuracy, 100% sensitivity, and 92.31% specificity. Moreover, features with high discriminative power to distinguish SD from MD showed a strong association with default mode network, frontoparietal network, affective network, and visual network regions.

LIMITATION

The sample size was relatively small, which may limit the application in clinical translation to some extent.

CONCLUSION

These findings demonstrated that a valid radiomics approach based on functional measures can discriminate SD from MD with a high classification performance, facilitating an objective and reliable diagnosis individually in clinical practice. Features with high discriminative power may provide insight into a profound understanding of the brain functional impairments and pathophysiology of SD and MD.

How structural and functional MRI can inform dual-site tACS parameters: A case study in a clinical population and its pragmatic implications

BACKGROUND

Abnormalities in frontoparietal network (FPN) were observed in many neuropsychiatric diseases including substance use disorders. A growing number of studies are using dual-site-tACS with frontoparietal synchronization to engage this network. However, a computational pathway to inform and optimize parameter space for frontoparietal synchronization is still lacking. In this case study, in a group of participants with methamphetamine use disorders, we proposed a computational pathway to extract optimal electrode montage while accounting for stimulation intensity using structural and functional MRI.

METHODS

Sixty methamphetamine users completed an fMRI drug cue-reactivity task. Four main steps were taken to define electrode montage and adjust stimulation intensity using 4x1 high-definition (HD) electrodes for a dual-site-tACS; (1) Frontal seed was defined based on the maximum electric fields (EF) predicted by simulation of HD montage over DLPFC (F3/F4 in EEG 10-20), (2) frontal seed-to-whole brain context-dependent correlation was calculated to determine connected regions to frontal seeds, (3) center of connected cluster in parietal cortex was selected as a location for placing the second set of HD electrodes to shape the informed montage, (4) individualized head models were used to determine optimal stimulation intensity considering underlying brain structure. The informed montage was compared to montages with large electrodes and classic frontoparietal HD montages (F3-P3/F4-P4) in terms of tACS-induced EF and ROI-to-ROI task-based/resting-state connectivity.

RESULTS

Compared to the large electrodes, HD frontoparietal montages allow for a finer control of the spatial peak fields in the main nodes of the FPN at the cost of lower maximum EF (large-pad/HD: max EF[V/m] = 0.37/0.11, number of cortical sub-regions that EF exceeds 50% of the max = 77/13). For defining stimulation targets based on EF patterns, using group-level head models compared to a single standard head model results in comparable but significantly different seed locations (6.43mm Euclidean distance between the locations of the frontal maximum EF in standard-space). As expected, significant task-based/resting-state connections were only found between frontal-parietal locations in the informed montage. Cue-induced craving score was correlated with frontoparietal connectivity only in the informed montage (r = -0.24). Stimulation intensity in the informed montage, and not in the classic HD montage, needs 40% reduction in the parietal site to reduce the disparity in EF between sites.

CONCLUSION

This study provides some empirical insights to montage and dose selection in dual-site-tACS using individual brain structures and functions and proposes a computational pathway to use head models and functional MRI to define (1) optimum electrode montage for targeting FPN in a context of interest (drug-cue-reactivity) and (2) proper transcranial stimulation intensity.

Dynamic changes of large-scale resting-state functional networks in major depressive disorder

Sliding window method is widely used to study the functional connectivity dynamics in brain networks. A key issue of this method is how to choose the window length and number of clusters across different window length. Here, we introduced a universal method to determine the optimal window length and number of clusters and applied it to study the dynamic functional network connectivity (FNC) in major depressive disorder (MDD). Specifically, we first extracted the resting-state networks (RSNs) in 27 medication-free MDD patients and 54 healthy controls using group independent component analysis (ICA), and constructed the dynamic FNC patterns for each subject in the window range of 10-80 repetition times (TRs) using sliding window method. Then, litekmeans algorithm was utilized to cluster the FNC patterns corresponding to each window length into 2-20 clusters. The optimal number of clusters was determined by voting method and the optimal window length was determined by identifying the most representative window length. Finally, 8 recurring FNC patterns regarded as FNC states were captured for further analyzing the dynamic attributes. Our results revealed that MDD patients showed increased mean dwell time and fraction of time spent in state #5, and the mean dwell time is correlated with depression symptom load. Additionally, compared with healthy controls, MDD patients had significantly reduced FNC within FPN in state #7. Our study reported a new approach to determine the optimal window length and number of clusters, which may facilitate the future study of the functional dynamics. These findings about MDD using dynamic FNC analyses provide new evidence to better understand the neuropathology of MDD.

Neural connectivity biotypes: associations with internalizing problems throughout adolescence

BACKGROUND

Neurophysiological patterns may distinguish which youth are at risk for the well-documented increase in internalizing symptoms during adolescence. Adolescents with internalizing problems exhibit altered resting-state functional connectivity (RSFC) of brain regions involved in socio-affective processing. Whether connectivity-based biotypes differentiate adolescents' levels of internalizing problems remains unknown.

METHOD

Sixty-eight adolescents (37 females) reported on their internalizing problems at ages 14, 16, and 18 years. A resting-state functional neuroimaging scan was collected at age 16. Time-series data of 15 internalizing-relevant brain regions were entered into the Subgroup-Group Iterative Multi-Model Estimation program to identify subgroups based on RSFC maps. Associations between internalizing problems and connectivity-based biotypes were tested with regression analyses.

RESULTS

Two connectivity-based biotypes were found: a Diffusely-connected biotype (N = 46), with long-range fronto-parietal paths, and a Hyper-connected biotype (N = 22), with paths between subcortical and medial frontal areas (e.g. affective and default-mode network regions). Higher levels of past (age 14) internalizing problems predicted a greater likelihood of belonging to the Hyper-connected biotype at age 16. The Hyper-connected biotype showed higher levels of concurrent problems (age 16) and future (age 18) internalizing problems.

CONCLUSIONS

Differential patterns of RSFC among socio-affective brain regions were predicted by earlier internalizing problems and predicted future internalizing problems in adolescence. Measuring connectivity-based biotypes in adolescence may offer insight into which youth face an elevated risk for internalizing disorders during this critical developmental period.

Depressive and anxiety symptoms are related to decreased lateral prefrontal cortex functioning during cognitive control in older people

Some studies have found a relationship between negative emotional symptoms and decreased lateral PFC functioning during a cognitive control task in healthy younger adults. Here, we asked whether this relationship is also present in the general older population and across different functional domains of the lateral PFC. Thirty-six older people (13 males) self-reported their recent depressive and anxiety symptoms. They also took two cognitive control tasks known to differentially engage the lateral frontoparietal network (digit n-back task) and the lateral frontotemporal network (Category Fluency Test) while hemodynamic changes in the PFC were monitored by functional near-infrared spectroscopy (fNIRS). Both depressive and anxiety symptoms were associated with decreased activation in the bilateral lateral PFC during cognitive control performance. Interestingly, these relationships were driven by the n-back task. Our findings suggest that depressive and anxiety symptoms are related to decreased lateral PFC functioning in particular domains of cognitive control among older people.

Mindfulness and Behavior Change

Initiating and maintaining behavior change is key to the prevention and treatment of most preventable chronic medical and psychiatric illnesses. The cultivation of mindfulness, involving acceptance and nonjudgment of present-moment experience, often results in transformative health behavior change. Neural systems involved in motivation and learning have an important role to play. A theoretical model of mindfulness that integrates these mechanisms with the cognitive, emotional, and self-related processes commonly described, while applying an integrated model to health behavior change, is needed. This integrative review (1) defines mindfulness and describes the mindfulness-based intervention movement, (2) synthesizes the neuroscience of mindfulness and integrates motivation and learning mechanisms within a mindful self-regulation model for understanding the complex effects of mindfulness on behavior change, and (3) synthesizes current clinical research evaluating the effects of mindfulness-based interventions targeting health behaviors relevant to psychiatric care. The review provides insight into the limitations of current research and proposes potential mechanisms to be tested in future research and targeted in clinical practice to enhance the impact of mindfulness on behavior change.

Disorganized resting-state functional connectivity between the dorsal attention network and intrinsic networks in Parkinson's disease with freezing of gait

Freezing of gait (FOG) is a common and complex manifestation of Parkinson's disease (PD) and is associated with impairment of attention. The purpose of this study was to evaluate the functional network connectivity (FNc) changes between the dorsal attention network (DAN) and the other seven intrinsic networks relevant to attention, visual-spatial, executive and motor functions in PD with or without FOG. Forty-three idiopathic PD patients (21 with FOG [FOG+] versus 22 without FOG [FOG-]) and 18 healthy controls (HC) were recruited in this study. The data-driven independent component analysis (ICA) method was used to extract and analyze the above-mentioned resting-state networks (RSNs). Compared with FOG-, FOG+ displayed decreased positive connectivity between the DAN and medial visual network (mVN) and sensory-motor network (SMN) and increased negative connectivity between the DAN and default mode network (DMN). The within-network connectivity in the SMN and visual networks were decreased, whereas the connectivity within DMN was increased significantly in FOG+. Correlation analysis showed that the clock drawing test (CDT) scores were positively correlated with the functional connectivity of mVN (r = 0.573, p = 0.008) and lateral visual network (lVN) (r = 0.510, p = 0.022), the Timed Up and Go Test (TUG) duration were negatively correlated with the connectivity of SMN (r = -0.629, p = 0.003), and the Frontal Assessment Battery (FAB) scores were negatively correlated with the connectivity of DMN in FOG+. Functional connectivity was changed in multiple intra-networks in patients with FOG. Inordinate inter-network connectivity between the DAN and other intrinsic networks may partly contribute to the mechanism of freezing.

Neural Correlates of Mild Behavioral Impairment: A Functional Brain Connectivity Study Using Resting-State Functional Magnetic Resonance Imaging

Background:

Mild behavioral impairment (MBI) is associated with accelerated cognitive decline and greater risk of dementia. However, the neural correlates of MBI have not been completely elucidated.

Objective:

The study aimed to investigate the correlation between cognitively normal participants and participants with amnestic mild cognitive impairment (aMCI) using resting-state functional magnetic resonance imaging.

Methods:

The study included 30 cognitively normal participants and 13 participants with aMCI (20 men and 23 women; mean age, 76.9 years). The MBI was assessed using the MBI checklist (MBI-C). Region of interest (ROI)-to-ROI analysis was performed to examine the correlation between MBI-C scores and functional connectivity (FC) of the default mode network, salience network, and frontoparietal control network (FPCN). Age, Mini-Mental State Examination score, sex, and education were used as covariates. A p-value of 0.05, with false discovery rate correction, was considered significant.

Results:

A negative correlation was observed between the MBI-C total score and FC of the left posterior parietal cortex with the right middle frontal gyrus. A similar result was obtained for the MBI-C affective dysregulation domain score.

Conclusion:

FPCN dysfunction was detected as a neural correlate of MBI, especially in the affective dysregulation domain. This dysfunction may be associated with cognitive impairment in MBI and conversion of MBI to dementia; however, further longitudinal data are needed to examine this relationship.

Prefrontal tDCS Attenuates Self-Referential Attentional Deployment: A Mechanism Underlying Adaptive Emotional Reactivity to Social-Evaluative Threat

Social-evaluative threat (SET) - a situation in which one could be negatively evaluated by others - elicits profound (psycho)physiological reactivity which, if chronically present and not adaptively regulated, has deleterious effects on mental and physical health. Decreased self-awareness and increased other-awareness are understood to be an adaptive response to SET. Attentional deployment - the process of selectively attending to certain aspects of emotional stimuli to modulate emotional reactivity - is supported by fronto-parietal and fronto-limbic networks, with the dorsolateral prefrontal cortex being a central hub. The primary aim of the current study was to investigate the effects of active (versus sham) prefrontal transcranial direct current stimulation (tDCS) on self and other-attentional deployment during the exposure to a SET context. Seventy-four female participants received active or sham tDCS and were subsequently exposed to a rigged social feedback paradigm. In this paradigm a series of social evaluations were presented together with a photograph of the supposed evaluator and a self- photograph of the participant, while gaze behavior (time to first fixation, total fixation time) and skin conductance responses (SCRs; a marker of emotional reactivity) were measured. For half of the evaluations, participants could anticipate the valence (negative or positive) of the evaluation a priori. Analyses showed that participants receiving active tDCS were (a) slower to fixate on their self-photograph, (b) spent less time fixating on their self-photograph, and (c) spent more time fixating on the evaluator photograph. During unanticipated evaluations, active tDCS was associated with less time spent fixating on the evaluation. Furthermore, among those receiving active tDCS, SCRs were attenuated as a function of slower times to fixate on the self-photograph. Taken together, these results suggest that in a context of SET, prefrontal tDCS decreases self-attention while increasing other-attention, and that attenuated self-referential attention specifically may be a neurocognitive mechanism through which tDCS reduces emotional reactivity. Moreover, the results suggest that tDCS reduces vigilance toward stimuli that possibly convey threatening information, corroborating past research in this area.

Brain network mechanisms of visual shape completion

Visual shape completion recovers object shape, size, and number from spatially segregated edges. Despite being extensively investigated, the process’s underlying brain regions, networks, and functional connections are still not well understood. To shed light on the topic, we scanned (fMRI) healthy adults during rest and during a task in which they discriminated pac-man configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Task activation differences (illusory-fragmented), resting-state functional connectivity, and multivariate patterns were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping (ActFlow) was used to evaluate the likely involvement of resting-state connections for shape completion. We identified 36 differentially-active parcels including a posterior temporal region, PH, whose activity was consistent across 95% of observers. Significant task regions primarily occupied the secondary visual network but also incorporated the frontoparietal dorsal attention, default mode, and cingulo-opercular networks. Each parcel’s task activation difference could be modeled via its resting-state connections with the remaining parcels (r=.62, p<10⁻⁹), suggesting that such connections undergird shape completion. Functional connections from the dorsal attention network were key in modelling task activation differences in the secondary visual network. Dorsal attention and frontoparietal connections could also model activations in the remaining networks. Taken together, these results suggest that shape completion relies upon a sparsely distributed but densely interconnected network coalition that is centered in the secondary visual network, coordinated by the dorsal attention network, and inclusive of at least three other networks.

Evidence of Age-Related Hemodynamic and Functional Connectivity Impairment: A Resting State fMRI Study

Purpose: To assess age-related changes in intrinsic functional brain connectivity and hemodynamics during adulthood in the context of the retrogenesis hypothesis, which states that the rate of age-related changes is higher in late-myelinating (prefrontal, lateral-posterior temporal) cerebrocortical areas as compared to early myelinating (parietal, occipital) regions. In addition, to examine the dependence of age-related changes upon concurrent subclinical depression symptoms which are common even in healthy aging. Methods: Sixty-four healthy adults (28 men) aged 23-79 years (mean 45.0, SD = 18.8 years) were examined. Resting-state functional MRI (rs-fMRI) time series were used to compute voxel-wise intrinsic connectivity contrast (ICC) maps reflecting the strength of functional connectivity between each voxel and the rest of the brain. We further used Time Shift Analysis (TSA) to estimate voxel-wise hemodynamic lead or lag for each of 22 ROIs from the automated anatomical atlas (AAL). Results: Adjusted for depression symptoms, gender and education level, reduced ICC with age was found primarily in frontal, temporal regions, and putamen, whereas the opposite trend was noted in inferior occipital cortices (p < 0.002). With the same covariates, increased hemodynamic lead with advancing age was found in superior frontal cortex and thalamus, with the opposite trend in inferior occipital cortex (p < 0.002). There was also evidence of reduced coupling between voxel-wise intrinsic connectivity and hemodynamics in the inferior parietal cortex. Conclusion: Age-related intrinsic connectivity reductions and hemodynamic changes were demonstrated in several regions-most of them part of DMN and salience networks-while impaired neurovascular coupling was, also, found in parietal regions. Age-related reductions in intrinsic connectivity were greater in anterior as compared to posterior cortices, in line with implications derived from the retrogenesis hypothesis. These effects were affected by self-reported depression symptoms, which also increased with age.

Patterns of brain activity during a set-shifting task linked to mild behavioral impairment in Parkinson's disease

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PD with mild behavioral impairment revealed deficits in cognitive flexibility.

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Brain activities during a set-shifting task linked with MBI in PD was evaluated.

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PD-MBI revealed reduced activity in the prefrontal and posterior parietal cortices.

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The prefrontal activity was associated with cognitive impairment in PD-MBI.

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High MBI-C score was associated with reduced deactivation in the hippocampus.

Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of sustained neuropsychiatric symptoms, as an at-risk state for incident cognitive decline and dementia. Prior studies have reported that neuropsychiatric symptoms are associated with cognitive abilities in Parkinson’s disease (PD) patients, and we have recently found a strong correlation between MBI and cognitive performance. However, the underlying neural activity patterns of cognitive performance linked to MBI in PD are unknown. Fifty-nine non-demented PD patients and 26 healthy controls were scanned using fMRI during performance of a modified version of the Wisconsin card sorting task. MBI was evaluated using the MBI-checklist, and PD patients were divided into two groups, PD-MBI and PD-noMBI. Compared to the PD-noMBI group and healthy controls, the PD-MBI group revealed less activation in the prefrontal and posterior parietal cortices, and reduced deactivation in the medial temporal region. These results suggest that in PD, MBI reflects deficits in the frontoparietal control network and the hippocampal memory system.

An Evolutionary Perspective of Dyslexia, Stress, and Brain Network Homeostasis

Evolution fuels interindividual variability in neuroplasticity, reflected in brain anatomy and functional connectivity of the expanding neocortical regions subserving reading ability. Such variability is orchestrated by an evolutionarily conserved, competitive balance between epigenetic, stress-induced, and cognitive-growth gene expression programs. An evolutionary developmental model of dyslexia, suggests that prenatal and childhood subclinical stress becomes a risk factor for dyslexia when physiological adaptations to stress promoting adaptive fitness, may attenuate neuroplasticity in the brain regions recruited for reading. Stress has the potential to blunt the cognitive-growth functions of the predominantly right hemisphere Ventral and Dorsal attention networks, which are primed with high entropic levels of synaptic plasticity, and are critical for acquiring beginning reading skills. The attentional networks, in collaboration with the stress-responsive Default Mode network, modulate the entrainment and processing of the low frequency auditory oscillations (1-8 Hz) and visuospatial orienting linked etiologically to dyslexia. Thus, dyslexia may result from positive, but costly adaptations to stress system dysregulation: protective measures that reset the stress/growth balance of processing to favor the Default Mode network, compromising development of the attentional networks. Such a normal-variability conceptualization of dyslexia is at odds with the frequent assumption that dyslexia results from a neurological abnormality. To put the normal-variability model in the broader perspective of the state of the field, a traditional evolutionary account of dyslexia is presented to stimulate discussion of the scientific merits of the two approaches.

Reduced Brain Gray Matter Volume in Patients With First-Episode Major Depressive Disorder: A Quantitative Meta-Analysis

Background: The findings of many neuroimaging studies in patients with first-episode major depressive disorder (MDD), and even those of previous meta-analysis, are divergent. To quantitatively integrate these studies, we performed a meta-analysis of gray matter volumes using voxel-based morphometry (VBM). Methods: We performed a comprehensive literature search for relevant studies and traced the references up to May 1, 2021 to select the VBM studies between first-episode MDD and healthy controls (HC). A quantitative meta-analysis of VBM studies on first-episode MDD was performed using the Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) method, which allows a familywise error rate (FWE) correction for multiple comparisons of the results. Meta-regression was used to explore the effects of demographics and clinical characteristics. Results: Nineteen studies, with 22 datasets comprising 619 first-episode MDD and 707 HC, were included. The pooled and subgroup meta-analysis showed robust gray matter reductions in the left insula, the bilateral parahippocampal gyrus extending into the bilateral hippocampus, the right gyrus rectus extending into the right striatum, the right superior frontal gyrus (dorsolateral part), the left superior frontal gyrus (medial part) and the left superior parietal gyrus. Meta-regression analyses showed that higher HDRS scores were significantly more likely to present reduced gray matter volumes in the right amygdala, and the mean age of MDD patients in each study was negatively correlated with reduced gray matter in the left insula. Conclusions: The present meta-analysis revealed that structural abnormalities in the fronto-striatal-limbic and fronto-parietal networks are essential characteristics in first-episode MDD patients, which may become a potential target for clinical intervention.

Intrinsic functional network contributions to the relationship between trait empathy and subjective happiness

Subjective happiness (well-being) is a multi-dimensional construct indexing one's evaluations of everyday emotional experiences and life satisfaction, and has been associated with different aspects of trait empathy. Despite previous research identifying the neural substrates of subjective happiness and empathy, the mechanisms mediating the relationship between the two constructs remain largely unclear. Here, we performed a data-driven, multi-voxel pattern analysis of whole-brain intrinsic functional connectivity to reveal the neural mechanisms of subjective happiness and trait empathy in a sample of young females. Behaviorally, we found that subjective happiness was negatively associated with personal distress (i.e., self-referential experience of others' feelings). Consistent with this inverse relationship, subjective happiness was associated with the dorsolateral prefrontal cortex exhibiting decreased functional connectivity with regions important for the representation of unimodal sensorimotor information (e.g., primary sensory cortices) or multi-modal summaries of brain states (e.g., default mode network) and increased functional connectivity with regions important for the attentional modulation of these representations (e.g., frontoparietal, attention networks). Personal distress was associated with the medial prefrontal cortex exhibiting functional connectivity differences with similar networks--but in the opposite direction. Finally, intrinsic functional connectivity within and between these networks fully mediated the relationship between the two behavioral measures. These results identify an important contribution of the macroscale functional organization of the brain to human well-being, by demonstrating that lower levels of personal distress lead to higher subjective happiness through variation in intrinsic functional connectivity along a neural representation vs. modulation gradient.

Device-Based Modulation of Neurocircuits as a Therapeutic for Psychiatric Disorders

Device-based neuromodulation of brain circuits is emerging as a promising new approach in the study and treatment of psychiatric disorders. This work presents recent advances in the development of tools for identifying neurocircuits as therapeutic targets and in tools for modulating neurocircuits. We review clinical evidence for the therapeutic efficacy of circuit modulation with a range of brain stimulation approaches, including subthreshold, subconvulsive, convulsive, and neurosurgical techniques. We further discuss strategies for enhancing the precision and efficacy of neuromodulatory techniques. Finally, we survey cutting-edge research in therapeutic circuit modulation using novel paradigms and next-generation devices.

Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder

Major depressive disorder is a debilitating condition with diverse neuroimaging correlates, including cortical thinning in medial prefrontal cortex and altered functional connectivity of cortical association networks. However, the molecular bases of these imaging markers remain ambiguous, despite a need for treatment targets and mechanisms. Here, we advance cross-modal approaches to identify cell types and gene transcripts associated with depression-implicated cortex. Across multiple population-imaging datasets (combined N ≥ 23,723) and ex vivo patient cortical tissue, somatostatin interneurons and astrocytes emerge as replicable cell-level correlates of depression and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging markers of depression and offer a roadmap for integrating in vivo clinical imaging with genetic and postmortem patient transcriptional data.

Major depressive disorder emerges from the complex interactions of biological systems that span genes and molecules through cells, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to depression requires a multiscale approach, encompassing measures of brain structure and function as well as genetic and cell-specific transcriptional data. Here, we examine anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Brain Genomics Superstruct Project, and Enhancing NeuroImaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene expression, postmortem patient transcriptional data, depression genome-wide association study (GWAS), and single-cell gene transcription. Neuroimaging correlates of depression and negative affect were consistent across three independent datasets. Linking ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes track gene down-regulation in postmortem cortical samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data reveal somatostatin interneurons and astrocytes to be consistent cell associates of depression, through both in vivo imaging and ex vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS-derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multiscale approaches, the transcriptional correlates of depression-linked brain function and structure were enriched for disorder-relevant molecular pathways. These findings bridge levels to connect specific genes, cell classes, and biological pathways to in vivo imaging correlates of depression.

The Dynamics of Functional Brain Networks Associated With Depressive Symptoms in a Nonclinical Sample

Brain function depends on the flexible and dynamic coordination of functional subsystems within distributed neural networks operating on multiple scales. Recent progress has been made in the characterization of functional connectivity (FC) at the whole-brain scale from a dynamic, rather than static, perspective, but its validity for cognitive sciences remains under debate. Here, we analyzed brain activity recorded with functional Magnetic Resonance Imaging from 71 healthy participants evaluated for depressive symptoms after a relationship breakup based on the conventional Major Depression Inventory (MDI). We compared both static and dynamic FC patterns between participants reporting high and low depressive symptoms. Between-group differences in static FC were estimated using a standard pipeline for network-based statistic (NBS). Additionally, FC was analyzed from a dynamic perspective by characterizing the occupancy, lifetime, and transition profiles of recurrent FC patterns. Recurrent FC patterns were defined by clustering the BOLD phase-locking patterns obtained using leading eigenvector dynamics analysis (LEiDA). NBS analysis revealed a brain subsystem exhibiting significantly lower within-subsystem correlation values in more depressed participants (high MDI). This subsystem predominantly comprised connections between regions of the default mode network (i.e., precuneus) and regions outside this network. On the other hand, LEiDA results showed that high MDI participants engaged more in a state connecting regions of the default mode, memory retrieval, and frontoparietal network (p-FDR = 0.012); and less in a state connecting mostly the visual and dorsal attention systems (p-FDR = 0.004). Although both our analyses on static and dynamic FC implicate the role of the precuneus in depressive symptoms, only including the temporal evolution of BOLD FC helped to disentangle over time the distinct configurations in which this region plays a role. This finding further indicates that a holistic understanding of brain function can only be gleaned if the temporal dynamics of FC is included.

Flexible Coordinator and Switcher Hubs for Adaptive Task Control

Functional connectivity (FC) studies have identified at least two large-scale neural systems that constitute cognitive control networks, the frontoparietal network (FPN) and cingulo-opercular network (CON). Control networks are thought to support goal-directed cognition and behavior. It was previously shown that the FPN flexibly shifts its global connectivity pattern according to task goal, consistent with a "flexible hub" mechanism for cognitive control. Our aim was to build on this finding to develop a functional cartography (a multimetric profile) of control networks in terms of dynamic network properties. We quantified network properties in (male and female) humans using a high-control-demand cognitive paradigm involving switching among 64 task sets. We hypothesized that cognitive control is enacted by the FPN and CON via distinct but complementary roles reflected in network dynamics. Consistent with a flexible "coordinator" mechanism, FPN connections were varied across tasks, while maintaining within-network connectivity to aid cross-region coordination. Consistent with a flexible "switcher" mechanism, CON regions switched to other networks in a task-dependent manner, driven primarily by reduced within-network connections to other CON regions. This pattern of results suggests FPN acts as a dynamic, global coordinator of goal-relevant information, while CON transiently disbands to lend processing resources to other goal-relevant networks. This cartography of network dynamics reveals a dissociation between two prominent cognitive control networks, suggesting complementary mechanisms underlying goal-directed cognition.SIGNIFICANCE STATEMENT Cognitive control supports a variety of behaviors requiring flexible cognition, such as rapidly switching between tasks. Furthermore, cognitive control is negatively impacted in a variety of mental illnesses. We used tools from network science to characterize the implementation of cognitive control by large-scale brain systems. This revealed that two systems, the frontoparietal (FPN) and cingulo-opercular (CON) networks, have distinct but complementary roles in controlling global network reconfigurations. The FPN exhibited properties of a flexible coordinator (orchestrating task changes), while CON acted as a flexible switcher (switching specific regions to other systems to lend processing resources). These findings reveal an underlying distinction in cognitive processes that may be applicable to clinical, educational, and machine learning work targeting cognitive flexibility.

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-based framework with machine learning algorithms, we demonstrate this framework's ability to predict individual brain age in a large sample of middle-to-late age adults, and highlight its clinical specificity for several disease populations from a network perspective. A proposed estimator with 40 SCNs could predict individual brain age, balancing between model complexity and prediction accuracy. Notably, we found that the most significant SCN for predicting brain age included the caudate nucleus, putamen, hippocampus, amygdala, and cerebellar regions. Furthermore, our data indicate a larger brain age disparity in patients with schizophrenia and Alzheimer's disease than in healthy controls, while this metric did not differ significantly in patients with major depressive disorder. These findings provide empirical evidence supporting the estimation of brain age from a brain network perspective, and demonstrate the clinical feasibility of evaluating neurological diseases hypothesized to be associated with accelerated brain aging.

Reduced spatiotemporal brain dynamics are associated with increased depressive symptoms after a relationship breakup

Depressive symptoms following a stressful life event, such as a relationship breakup, are common, and constitute a potent risk factor for the onset of a major depressive episode. Resting-state neuroimaging studies have increasingly identified abnormal whole-brain communication in patients with depression, but it is currently unclear whether depressive symptoms in individuals without a clinical diagnosis have reliable neural underpinnings. We investigated to what extent the severity of depressive symptoms in a non-clinical sample was associated with imbalances in the complex dynamics of the brain during rest. To this end, a novel intrinsic ignition approach was applied to resting-state neuroimaging data from sixty-nine participants with varying degrees of depressive symptoms following a relationship breakup. Ignition-based measures of integration, hierarchy, and metastability were calculated for each participant, revealing a negative correlation between these measures and depressive ratings. We found that the severity of depressive symptoms was associated with deficits in the brain's capacity to globally integrate and process information over time. Furthermore, we found that increased depressive symptoms were associated with reduced spatial diversity (i.e., hierarchy) and reduced temporal variability (i.e., metastability) in the functional organization of the brain. These findings suggest the merit of investigating constrained dynamical complexity as it is sensitive to the level of depressive symptoms even in a non-clinical sample.

Dyslexia as an adaptation to cortico-limbic stress system reactivity

A new school of thought in evolutionary developmental biology, combined with research in the neurobiology of stress, suggest that early exposure to stressful circumstances may be a cause of dyslexia. A balance between epigenetic, stress-induced and cognitive-growth genetic programs modulates the brain's cellular, regional, and network homeostasis. This balance is essential for adaptability to the normative range of everyday stress. However, even mild chronic stress exposition may overactivate the hypothalmic-pituitary-adrenal stress axis, upsetting the homeostatic balance between these programs, and exposing the brain to harmful levels of stress hormones. A protective strategy to sustained disequilibrium precociously advances maturation at the cost of neuroplasticity, which blunts stress axis reactivity but also compromises learning potential in the prefrontal cortex and networks associated with dyslexia. Stress exceeding an individual's range of resilience: (1) reduces levels of TFEB and BDNF, gene regulatory factors prolonging maturation and neuroplasticity; (2) interferes with the insular cortex, amygdala and hippocampus in coordinating afferent visceral signals with cognitive performance; (3) over-recruits the brain's Default Mode network; and (4) amplifies release from the Locus coeruleus/norepinephrine system which impairs the entrainment of oscillations in the lower phonological frequencies of speech. Evidence supporting a stress-growth imbalance is preliminary, but holds promise for reconceptualizing the neurobiology of dyslexia and reducing its prevalence.

A Unique Brain Connectome Fingerprint Predates and Predicts Response to Antidepressants

More than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. In an attempt to solve this mystery, rather than studying the acute neurochemical effects of antidepressants, here we propose focusing on the early changes in the brain functional connectome using traditional statistics and machine learning approaches. Capitalizing on three independent datasets (n = 1,261) and recent developments in data and network science, we identified a specific connectome fingerprint that predates and predicts response to monoaminergic antidepressants. The discovered fingerprint appears to generalize to antidepressants with differing mechanism of action. We also established a consensus whole-brain hierarchical connectivity architecture and provided a set of model-based features engineering approaches suitable for identifying connectomic signatures of brain function in health and disease.

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Machine learning methods were used to fully investigate the brain connectome

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Network-informed features engineering approaches were proposed

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A cortical-subcortical hierarchical brain atlas was established

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A specific connectome signature was found to predict response to antidepressants

A graph representation of functional diversity of brain regions

INTRODUCTION

Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task-based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be understood.

METHODS

Here, we present a novel graph measure, the neighbor dispersion index, to test the hypothesis that the functional diversity of a brain region is embodied by the topological dissimilarity of its immediate neighbors in the large-scale functional brain network.

RESULTS

We consistently identified in two independent and publicly accessible resting-state functional magnetic resonance imaging datasets that brain regions in the frontoparietal and salience networks showed higher neighbor dispersion index, whereas those in the visual, auditory, and sensorimotor networks showed lower neighbor dispersion index. Moreover, we observed that human fluid intelligence was associated with the neighbor dispersion index of dorsolateral prefrontal cortex, while no such association for the other metrics commonly used for characterizing network hubs was noticed even with an uncorrected p < .05.

CONCLUSIONS

This newly developed graph theoretical method offers fresh insight into the topological organization of functional brain networks and also sheds light on individual differences in human intelligence.

A computational network control theory analysis of depression symptoms

Rumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.